2025-08-28 10:08:15.551 | INFO     | yolox_microbt.core.trainer:before_train:88 - args: Namespace(config='configs.sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger', experiment_name='sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger', name=None, dist_backend='nccl', dist_url=None, batch_size=64, devices=8, exp_file=None, resume=False, ckpt=None, start_epoch=None, num_machines=1, machine_rank=0, fp16=False, cache=None, occupy=False, logger='tensorboard', opts=[])
2025-08-28 10:08:15.557 | INFO     | yolox_microbt.core.trainer:before_train:89 - exp value:
╒═══════════════════╤════════════════════════════════════════════════════════════════════════╕
│ keys              │ values                                                                 │
╞═══════════════════╪════════════════════════════════════════════════════════════════════════╡
│ seed              │ None                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ output_dir        │ './YOLOX_outputs'                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ print_interval    │ 20                                                                     │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ eval_interval     │ 1                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ dataset           │ None                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ num_classes       │ 3                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ depth             │ 1.0                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ width             │ 1.0                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ act               │ 'silu'                                                                 │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ data_num_workers  │ 2                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ input_size        │ (416, 416)                                                             │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ multiscale_range  │ 5                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ data_dir          │ None                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ train_ann         │ 'instances_train2017.json'                                             │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ val_ann           │ 'instances_val2017.json'                                               │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ test_ann          │ 'instances_test2017.json'                                              │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ mosaic_prob       │ 1.0                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ mixup_prob        │ 0.5                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ hsv_prob          │ 0.5                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ flip_prob         │ 0.5                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ degrees           │ 10.0                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ translate         │ 0.1                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ mosaic_scale      │ (0.1, 2)                                                               │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ enable_mixup      │ True                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ mixup_scale       │ (0.5, 1.5)                                                             │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ shear             │ 2.0                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ warmup_epochs     │ 0                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ max_epoch         │ 600                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ warmup_lr         │ 0                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ min_lr_ratio      │ 0.05                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ basic_lr_per_img  │ 3.125e-05                                                              │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ scheduler         │ 'warmcos'                                                              │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ no_aug_epochs     │ 400                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ ema               │ False                                                                  │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ weight_decay      │ 0.0005                                                                 │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ momentum          │ 0.9                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ save_history_ckpt │ True                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ exp_name          │ 'sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger' │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ test_size         │ (416, 416)                                                             │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ test_conf         │ 0.01                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ nmsthre           │ 0.65                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ qat_warmup_epoch  │ 0                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ qat_clib_epoch    │ 2                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ all_reduce_norm   │ False                                                                  │
╘═══════════════════╧════════════════════════════════════════════════════════════════════════╛
2025-08-28 10:08:16.450 | INFO     | yolox_microbt.core.trainer:before_train:129 - init prefetcher, this might take one minute or less...
2025-08-28 10:08:29.929 | INFO     | yolox_microbt.core.trainer:before_train:88 - args: Namespace(config='configs.sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger', experiment_name='sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger', name=None, dist_backend='nccl', dist_url=None, batch_size=64, devices=8, exp_file=None, resume=False, ckpt=None, start_epoch=None, num_machines=1, machine_rank=0, fp16=False, cache=None, occupy=False, logger='tensorboard', opts=[])
2025-08-28 10:08:29.934 | INFO     | yolox_microbt.core.trainer:before_train:89 - exp value:
╒═══════════════════╤════════════════════════════════════════════════════════════════════════╕
│ keys              │ values                                                                 │
╞═══════════════════╪════════════════════════════════════════════════════════════════════════╡
│ seed              │ None                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ output_dir        │ './YOLOX_outputs'                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ print_interval    │ 20                                                                     │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ eval_interval     │ 1                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ dataset           │ None                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ num_classes       │ 3                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ depth             │ 1.0                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ width             │ 1.0                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ act               │ 'silu'                                                                 │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ data_num_workers  │ 2                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ input_size        │ (416, 416)                                                             │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ multiscale_range  │ 5                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ data_dir          │ None                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ train_ann         │ 'instances_train2017.json'                                             │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ val_ann           │ 'instances_val2017.json'                                               │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ test_ann          │ 'instances_test2017.json'                                              │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ mosaic_prob       │ 1.0                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ mixup_prob        │ 0.5                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ hsv_prob          │ 0.5                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ flip_prob         │ 0.5                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ degrees           │ 10.0                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ translate         │ 0.1                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ mosaic_scale      │ (0.1, 2)                                                               │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ enable_mixup      │ True                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ mixup_scale       │ (0.5, 1.5)                                                             │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ shear             │ 2.0                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ warmup_epochs     │ 0                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ max_epoch         │ 600                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ warmup_lr         │ 0                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ min_lr_ratio      │ 0.05                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ basic_lr_per_img  │ 3.125e-05                                                              │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ scheduler         │ 'warmcos'                                                              │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ no_aug_epochs     │ 400                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ ema               │ False                                                                  │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ weight_decay      │ 0.0005                                                                 │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ momentum          │ 0.9                                                                    │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ save_history_ckpt │ True                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ exp_name          │ 'sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger' │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ test_size         │ (416, 416)                                                             │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ test_conf         │ 0.01                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ nmsthre           │ 0.65                                                                   │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ qat_warmup_epoch  │ 0                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ qat_clib_epoch    │ 2                                                                      │
├───────────────────┼────────────────────────────────────────────────────────────────────────┤
│ all_reduce_norm   │ False                                                                  │
╘═══════════════════╧════════════════════════════════════════════════════════════════════════╛
2025-08-28 10:08:30.899 | INFO     | yolox_microbt.core.trainer:before_train:129 - init prefetcher, this might take one minute or less...
2025-08-28 10:08:34.223 | INFO     | yolox_microbt.core.trainer:before_train:168 - Training start...
2025-08-28 10:08:34.396 | INFO     | yolox_microbt.core.trainer:before_train:169 - 
DistributedDataParallel(
  (module): YOLOXTrainer(
    (yolox): GraphModule(
      (backbone0): Module(
        (backbone): Module(
          (0): Module(
            (0): Module(
              (conv): ConvReLU2d(
                3, 8, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
          )
          (1): Module(
            (0): Module(
              (conv_dw): ConvReLU2d(
                8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=8
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_pw): Conv2d(
                8, 10, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
          )
          (2): Module(
            (0): Module(
              (conv_pw): ConvReLU2d(
                10, 40, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_dw): ConvReLU2d(
                40, 40, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=40
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_pwl): Conv2d(
                40, 8, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
            (1): Module(
              (conv_pw): ConvReLU2d(
                8, 32, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_dw): ConvReLU2d(
                32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_pwl): Conv2d(
                32, 8, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
          )
          (3): Module(
            (0): Module(
              (conv_pw): ConvReLU2d(
                8, 32, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_dw): ConvReLU2d(
                32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_pwl): Conv2d(
                32, 10, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
            (1): Module(
              (conv_pw): ConvReLU2d(
                10, 40, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_dw): ConvReLU2d(
                40, 40, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=40
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_pwl): Conv2d(
                40, 10, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
          )
          (4): Module(
            (0): Module(
              (conv_pw): ConvReLU2d(
                10, 40, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_dw): ConvReLU2d(
                40, 40, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=40
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_pwl): Conv2d(
                40, 16, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
            (1): Module(
              (conv_pw): ConvReLU2d(
                16, 64, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_dw): ConvReLU2d(
                64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_pwl): Conv2d(
                64, 16, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
            (2): Module(
              (conv_pw): ConvReLU2d(
                16, 64, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_dw): ConvReLU2d(
                64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_pwl): Conv2d(
                64, 16, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
          )
          (5): Module(
            (0): Module(
              (conv_pw): ConvReLU2d(
                16, 64, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_dw): ConvReLU2d(
                64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_pwl): Conv2d(
                64, 48, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
          )
          (6): Module(
            (0): Module(
              (conv_pw): ConvReLU2d(
                48, 192, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_dw): ConvReLU2d(
                192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_pwl): Conv2d(
                192, 52, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
          )
          (7): Module(
            (0): Module(
              (conv_pw): ConvReLU2d(
                52, 208, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_dw): ConvReLU2d(
                208, 208, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=208
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_pwl): Conv2d(
                208, 88, kernel_size=(1, 1), stride=(1, 1), groups=2
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
            (1): Module(
              (conv_pw): ConvReLU2d(
                88, 352, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_dw): ConvReLU2d(
                352, 352, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=352
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_pwl): Conv2d(
                352, 88, kernel_size=(1, 1), stride=(1, 1), groups=2
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
          )
          (8): Module(
            (0): Module(
              (conv_pw): ConvReLU2d(
                88, 352, kernel_size=(1, 1), stride=(1, 1)
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_dw): ConvReLU2d(
                352, 352, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=352
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
              (conv_pwl): Conv2d(
                352, 144, kernel_size=(1, 1), stride=(1, 1), groups=2
                (weight_fake_quant): LearnableFakeQuantize(
                  fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                  tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                  tensor([0.], device='cuda:0')
                  (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
                )
              )
            )
          )
        )
      )
      (head0): Module(
        (shared_layer_8): Module(
          (conv0): Module(
            (conv0): ConvReLU2d(
              10, 64, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
          (conv1): Module(
            (conv0): ConvReLU2d(
              64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
          (conv2): Module(
            (conv0): ConvReLU2d(
              64, 64, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_8_obj): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 1, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_8_cls): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 3, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_8_box): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 4, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (shared_layer_16): Module(
          (conv0): Module(
            (conv0): ConvReLU2d(
              52, 64, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
          (conv1): Module(
            (conv0): ConvReLU2d(
              64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
          (conv2): Module(
            (conv0): ConvReLU2d(
              64, 64, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_16_obj): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 1, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_16_cls): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 3, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_16_box): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 4, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (shared_layer_32): Module(
          (conv0): Module(
            (conv0): ConvReLU2d(
              144, 64, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
          (conv1): Module(
            (conv0): ConvReLU2d(
              64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
          (conv2): Module(
            (conv0): ConvReLU2d(
              64, 64, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_32_obj): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 1, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_32_cls): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 3, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
        (layer_32_box): Module(
          (conv0): Module(
            (conv0): Conv2d(
              64, 4, kernel_size=(1, 1), stride=(1, 1)
              (weight_fake_quant): LearnableFakeQuantize(
                fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-8, quant_max=7, dtype=torch.qint8, qscheme=torch.per_channel_symmetric, ch_axis=0, is_per_channel=True, is_symmetric_quant=True, scale=Parameter containing:
                tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
                tensor([0.], device='cuda:0')
                (activation_post_process): MinMaxObserver(min_val=List, max_val=List, pot=False)
              )
            )
          )
        )
      )
      (x_post_act_fake_quantizer): FixedFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=0, quant_max=255, dtype=torch.quint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32)
        (activation_post_process): PseudoObserver(min_val=0.0, max_val=1.0, pot=False)
      )
      (backbone0_backbone_0_0_conv_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_1_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_1_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_1_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_1_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_2_1_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_1_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_1_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_3_1_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_1_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_1_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_1_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_1_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_2_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_2_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_2_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_4_2_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_3_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_5_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_5_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_5_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_6_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_6_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_6_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_7_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_7_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_7_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_7_1_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_7_1_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_7_1_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (add_4_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_8_0_conv_pw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_8_0_conv_dw_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_8_conv0_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_8_conv1_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_8_conv2_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_16_conv0_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_16_conv1_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_16_conv2_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (backbone0_backbone_8_0_conv_pwl_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_32_conv0_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_32_conv1_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_shared_layer_32_conv2_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_layer_8_obj_conv0_conv0_post_act_fake_quantizer): PseudoFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32) (test/eval)
        (activation_post_process): PseudoObserver(min_val=-8.0, max_val=8.0, pot=False)
      )
      (head0_layer_8_cls_conv0_conv0_post_act_fake_quantizer): PseudoFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32) (test/eval)
        (activation_post_process): PseudoObserver(min_val=-8.0, max_val=8.0, pot=False)
      )
      (head0_layer_8_box_conv0_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_layer_16_obj_conv0_conv0_post_act_fake_quantizer): PseudoFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32) (test/eval)
        (activation_post_process): PseudoObserver(min_val=-8.0, max_val=8.0, pot=False)
      )
      (head0_layer_16_cls_conv0_conv0_post_act_fake_quantizer): PseudoFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32) (test/eval)
        (activation_post_process): PseudoObserver(min_val=-8.0, max_val=8.0, pot=False)
      )
      (head0_layer_16_box_conv0_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
      (head0_layer_32_obj_conv0_conv0_post_act_fake_quantizer): PseudoFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32) (test/eval)
        (activation_post_process): PseudoObserver(min_val=-8.0, max_val=8.0, pot=False)
      )
      (head0_layer_32_cls_conv0_conv0_post_act_fake_quantizer): PseudoFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric, ch_axis=-1, scale=tensor([1.], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32) (test/eval)
        (activation_post_process): PseudoObserver(min_val=-8.0, max_val=8.0, pot=False)
      )
      (head0_layer_32_box_conv0_conv0_post_act_fake_quantizer): LearnableFakeQuantize(
        fake_quant_enabled=tensor([0], device='cuda:0', dtype=torch.uint8), observer_enabled=tensor([1], device='cuda:0', dtype=torch.uint8), is_initialized_params=tensor([0], device='cuda:0', dtype=torch.uint8), quant_min=-128, quant_max=127, dtype=torch.qint8, qscheme=torch.per_tensor_affine, ch_axis=-1, is_per_channel=False, is_symmetric_quant=False, scale=Parameter containing:
        tensor([1.], device='cuda:0', requires_grad=True), zero_point=Parameter containing:
        tensor([0.], device='cuda:0', requires_grad=True)
        (activation_post_process): EMAMinMaxObserver(min_val=inf, max_val=-inf, pot=False)
      )
    )
    (loss): YOLOXLoss(
      (l1_loss): L1Loss()
      (bcewithlog_loss): BCEWithLogitsLoss()
      (iou_loss): IOUloss()
    )
  )
)
2025-08-28 10:08:34.398 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch1
2025-08-28 10:08:34.415 | INFO     | yolox_microbt.core.trainer:before_epoch:200 - --->No mosaic aug for calibration model!
2025-08-28 10:08:38.445 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 1/600, iter: 20/129, gpu mem: 1367Mb, mem: 43.8Gb, iter_time: 0.201s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 2.000e-03, size: 576, ETA: 4:18:55
2025-08-28 10:08:41.266 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 1/600, iter: 40/129, gpu mem: 1367Mb, mem: 43.8Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.7, l1_loss: 0.0, conf_loss: 1.5, cls_loss: 0.5, lr: 2.000e-03, size: 576, ETA: 3:39:38
2025-08-28 10:08:44.458 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 1/600, iter: 60/129, gpu mem: 1367Mb, mem: 43.8Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.8, lr: 2.000e-03, size: 384, ETA: 3:34:18
2025-08-28 10:08:47.442 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 1/600, iter: 80/129, gpu mem: 1367Mb, mem: 43.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.6, lr: 2.000e-03, size: 320, ETA: 3:28:27
2025-08-28 10:08:49.965 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 1/600, iter: 100/129, gpu mem: 1367Mb, mem: 43.8Gb, iter_time: 0.124s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 2.000e-03, size: 384, ETA: 3:18:43
2025-08-28 10:08:52.721 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 1/600, iter: 120/129, gpu mem: 1367Mb, mem: 43.8Gb, iter_time: 0.136s, data_time: 0.002s, total_loss: 4.0, iou_loss: 2.1, l1_loss: 0.0, conf_loss: 1.3, cls_loss: 0.6, lr: 2.000e-03, size: 576, ETA: 3:14:48
2025-08-28 10:08:53.911 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:09:00.342 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:09:01.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:09:01.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6277
2025-08-28 10:09:01.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5690
2025-08-28 10:09:01.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4326
2025-08-28 10:09:01.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5431
2025-08-28 10:09:01.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:09:01.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:09:01.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.628
2025-08-28 10:09:01.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.569
2025-08-28 10:09:01.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.433
2025-08-28 10:09:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.543
2025-08-28 10:09:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:09:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:09:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:09:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:09:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:09:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:09:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:09:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:09:01.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:09:02.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:09:03.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:09:03.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:09:04.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:09:05.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:09:05.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:09:06.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:09:06.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:09:07.564 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:09:07.564 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.30
2025-08-28 10:09:07.564 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.54
2025-08-28 10:09:07.564 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:09:07.573 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.97 ms, Average inference time: 7.21 ms

2025-08-28 10:09:07.582 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:09:07.663 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:09:07.785 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch2
2025-08-28 10:09:07.791 | INFO     | yolox_microbt.core.trainer:before_epoch:200 - --->No mosaic aug for calibration model!
2025-08-28 10:09:10.476 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 2/600, iter: 20/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.132s, data_time: 0.002s, total_loss: 3.3, iou_loss: 1.8, l1_loss: 0.0, conf_loss: 1.0, cls_loss: 0.5, lr: 2.000e-03, size: 448, ETA: 3:09:51
2025-08-28 10:09:13.055 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 2/600, iter: 40/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.128s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.9, l1_loss: 0.0, conf_loss: 1.1, cls_loss: 0.5, lr: 2.000e-03, size: 448, ETA: 3:06:48
2025-08-28 10:09:15.642 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 2/600, iter: 60/129, gpu mem: 1367Mb, mem: 43.8Gb, iter_time: 0.129s, data_time: 0.002s, total_loss: 3.3, iou_loss: 1.4, l1_loss: 0.0, conf_loss: 1.3, cls_loss: 0.5, lr: 2.000e-03, size: 384, ETA: 3:04:30
2025-08-28 10:09:18.121 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 2/600, iter: 80/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.122s, data_time: 0.002s, total_loss: 2.7, iou_loss: 1.4, l1_loss: 0.0, conf_loss: 0.8, cls_loss: 0.5, lr: 2.000e-03, size: 384, ETA: 3:01:52
2025-08-28 10:09:20.614 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 2/600, iter: 100/129, gpu mem: 1367Mb, mem: 43.8Gb, iter_time: 0.123s, data_time: 0.001s, total_loss: 3.2, iou_loss: 1.7, l1_loss: 0.0, conf_loss: 1.1, cls_loss: 0.5, lr: 2.000e-03, size: 416, ETA: 2:59:49
2025-08-28 10:09:23.306 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 2/600, iter: 120/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.133s, data_time: 0.001s, total_loss: 3.4, iou_loss: 1.8, l1_loss: 0.0, conf_loss: 1.0, cls_loss: 0.6, lr: 2.000e-03, size: 288, ETA: 2:59:05
2025-08-28 10:09:24.643 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:09:30.891 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:09:31.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:09:31.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6184
2025-08-28 10:09:32.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5717
2025-08-28 10:09:32.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4031
2025-08-28 10:09:32.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5311
2025-08-28 10:09:32.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:09:32.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:09:32.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.618
2025-08-28 10:09:32.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-08-28 10:09:32.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-08-28 10:09:32.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.531
2025-08-28 10:09:32.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:09:32.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:09:32.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:09:32.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:09:32.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:09:32.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:09:32.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:09:32.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:09:32.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:09:32.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:09:33.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:09:33.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:09:34.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:09:35.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:09:36.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:09:36.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:09:37.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:09:37.902 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:09:37.902 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.29
2025-08-28 10:09:37.902 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.53
2025-08-28 10:09:37.902 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:09:37.912 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.90 ms, Average inference time: 7.09 ms

2025-08-28 10:09:37.930 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:09:38.008 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:09:38.089 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch3
2025-08-28 10:09:38.137 | INFO     | yolox_microbt.core.trainer:before_epoch:204 - --->enable mosaic aug for quantization training!
2025-08-28 10:09:41.292 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 3/600, iter: 20/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 2.000e-03, size: 480, ETA: 3:00:49
2025-08-28 10:09:44.563 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 3/600, iter: 40/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.2, lr: 2.000e-03, size: 448, ETA: 3:02:34
2025-08-28 10:09:47.901 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 3/600, iter: 60/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 2.000e-03, size: 352, ETA: 3:04:18
2025-08-28 10:09:51.153 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 3/600, iter: 80/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 2.000e-03, size: 448, ETA: 3:05:31
2025-08-28 10:09:54.862 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 3/600, iter: 100/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.183s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 2.000e-03, size: 512, ETA: 3:08:13
2025-08-28 10:09:58.151 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 3/600, iter: 120/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 2.000e-03, size: 544, ETA: 3:09:13
2025-08-28 10:09:59.633 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:10:05.936 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:10:08.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:10:09.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5572
2025-08-28 10:10:10.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5052
2025-08-28 10:10:10.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3741
2025-08-28 10:10:10.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4788
2025-08-28 10:10:10.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:10:10.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:10:10.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.557
2025-08-28 10:10:10.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-08-28 10:10:10.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.374
2025-08-28 10:10:10.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.479
2025-08-28 10:10:10.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:10:10.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:10:10.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:10:10.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:10:10.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:10:10.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:10:10.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:10:10.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:10:10.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:10:12.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:10:13.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:10:15.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:10:17.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:10:19.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:10:21.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:10:23.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:10:25.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:10:27.382 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:10:27.382 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 10:10:27.383 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 10:10:27.383 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:10:27.414 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.99 ms, Average inference time: 7.22 ms

2025-08-28 10:10:27.416 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:10:27.499 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:10:27.592 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch4
2025-08-28 10:10:30.734 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 4/600, iter: 20/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 2.000e-03, size: 544, ETA: 3:10:04
2025-08-28 10:10:33.895 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 4/600, iter: 40/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 2.000e-03, size: 256, ETA: 3:10:31
2025-08-28 10:10:37.281 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 4/600, iter: 60/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 2.000e-03, size: 512, ETA: 3:11:33
2025-08-28 10:10:40.506 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 4/600, iter: 80/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.8, lr: 2.000e-03, size: 512, ETA: 3:12:01
2025-08-28 10:10:43.815 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 4/600, iter: 100/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 2.000e-03, size: 288, ETA: 3:12:40
2025-08-28 10:10:47.076 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 4/600, iter: 120/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.0, lr: 2.000e-03, size: 288, ETA: 3:13:07
2025-08-28 10:10:48.498 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:10:54.732 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:10:57.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:10:58.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5698
2025-08-28 10:10:58.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5034
2025-08-28 10:10:59.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3625
2025-08-28 10:10:59.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4786
2025-08-28 10:10:59.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:10:59.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:10:59.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.570
2025-08-28 10:10:59.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-08-28 10:10:59.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-08-28 10:10:59.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.479
2025-08-28 10:10:59.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:10:59.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:10:59.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:10:59.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:10:59.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:10:59.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:10:59.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:10:59.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:10:59.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:11:01.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:11:02.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:11:04.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:11:06.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:11:08.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:11:10.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:11:12.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:11:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:11:16.365 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:11:16.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 10:11:16.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 10:11:16.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:11:16.394 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.97 ms, Average inference time: 7.17 ms

2025-08-28 10:11:16.395 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:11:16.479 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:11:16.562 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch5
2025-08-28 10:11:19.602 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 5/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 2.000e-03, size: 480, ETA: 3:13:06
2025-08-28 10:11:23.046 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 5/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 2.000e-03, size: 448, ETA: 3:13:55
2025-08-28 10:11:26.366 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 5/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.3, lr: 2.000e-03, size: 544, ETA: 3:14:25
2025-08-28 10:11:29.664 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 5/600, iter: 80/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 2.000e-03, size: 416, ETA: 3:14:51
2025-08-28 10:11:32.872 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 5/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 2.000e-03, size: 512, ETA: 3:15:03
2025-08-28 10:11:36.158 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 5/600, iter: 120/129, gpu mem: 1367Mb, mem: 43.9Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 2.000e-03, size: 352, ETA: 3:15:25
2025-08-28 10:11:37.627 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:11:43.972 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:11:47.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:11:50.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5555
2025-08-28 10:11:50.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4945
2025-08-28 10:11:50.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3695
2025-08-28 10:11:50.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4732
2025-08-28 10:11:50.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:11:50.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:11:50.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.556
2025-08-28 10:11:50.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-08-28 10:11:50.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-28 10:11:50.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.473
2025-08-28 10:11:50.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:11:50.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:11:50.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:11:50.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:11:50.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:11:50.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:11:50.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:11:50.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:11:50.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:11:54.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:11:57.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:12:00.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:12:03.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:12:07.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:12:10.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:12:13.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:12:16.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:12:19.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:12:19.494 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 10:12:19.494 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 10:12:19.494 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:12:19.520 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 1.00 ms, Average inference time: 7.24 ms

2025-08-28 10:12:19.532 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:12:19.659 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:12:19.746 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch6
2025-08-28 10:12:22.926 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 6/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 1.0, lr: 2.000e-03, size: 256, ETA: 3:15:36
2025-08-28 10:12:26.118 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 6/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.6, lr: 2.000e-03, size: 544, ETA: 3:15:42
2025-08-28 10:12:29.452 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 6/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 2.000e-03, size: 256, ETA: 3:16:03
2025-08-28 10:12:32.643 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 6/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 2.000e-03, size: 256, ETA: 3:16:07
2025-08-28 10:12:35.942 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 6/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 2.000e-03, size: 288, ETA: 3:16:22
2025-08-28 10:12:39.087 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 6/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 2.000e-03, size: 384, ETA: 3:16:20
2025-08-28 10:12:40.572 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:12:46.923 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:12:49.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:12:51.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5851
2025-08-28 10:12:51.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5218
2025-08-28 10:12:51.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3578
2025-08-28 10:12:51.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4882
2025-08-28 10:12:51.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:12:51.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:12:51.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.585
2025-08-28 10:12:51.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-08-28 10:12:51.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-08-28 10:12:51.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-08-28 10:12:51.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:12:51.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:12:51.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:12:51.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:12:51.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:12:51.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:12:51.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:12:51.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:12:51.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:12:53.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:12:55.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:12:57.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:12:59.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:13:01.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:13:03.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:13:05.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:13:07.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:13:09.494 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:13:09.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 10:13:09.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 10:13:09.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:13:09.520 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.93 ms, Average inference time: 7.16 ms

2025-08-28 10:13:09.522 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:13:09.612 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:13:09.697 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch7
2025-08-28 10:13:12.661 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 7/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.999e-03, size: 448, ETA: 3:16:09
2025-08-28 10:13:15.973 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 7/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.999e-03, size: 320, ETA: 3:16:24
2025-08-28 10:13:19.100 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 7/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.999e-03, size: 256, ETA: 3:16:23
2025-08-28 10:13:22.305 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 7/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.999e-03, size: 512, ETA: 3:16:28
2025-08-28 10:13:25.609 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 7/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.999e-03, size: 576, ETA: 3:16:40
2025-08-28 10:13:28.899 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 7/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.999e-03, size: 256, ETA: 3:16:51
2025-08-28 10:13:30.374 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:13:36.667 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:13:38.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:13:39.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5397
2025-08-28 10:13:39.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4625
2025-08-28 10:13:39.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3342
2025-08-28 10:13:39.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4455
2025-08-28 10:13:39.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:13:39.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:13:39.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.540
2025-08-28 10:13:39.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.463
2025-08-28 10:13:39.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-08-28 10:13:39.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.445
2025-08-28 10:13:39.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:13:39.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:13:39.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:13:39.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:13:39.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:13:39.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:13:39.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:13:39.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:13:39.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:13:41.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:13:42.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:13:44.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:13:45.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:13:47.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:13:48.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:13:49.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:13:51.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:13:52.670 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:13:52.671 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 10:13:52.671 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 10:13:52.671 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:13:52.681 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.94 ms, Average inference time: 7.17 ms

2025-08-28 10:13:52.684 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:13:52.758 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:13:52.847 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch8
2025-08-28 10:13:56.059 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 8/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.999e-03, size: 416, ETA: 3:16:58
2025-08-28 10:13:59.260 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 8/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.999e-03, size: 384, ETA: 3:17:00
2025-08-28 10:14:02.685 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 8/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.999e-03, size: 352, ETA: 3:17:20
2025-08-28 10:14:05.800 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 8/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.999e-03, size: 448, ETA: 3:17:14
2025-08-28 10:14:09.144 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 8/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.999e-03, size: 448, ETA: 3:17:27
2025-08-28 10:14:12.411 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 8/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.999e-03, size: 320, ETA: 3:17:32
2025-08-28 10:14:13.824 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:14:20.146 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:14:24.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:14:27.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5787
2025-08-28 10:14:27.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4837
2025-08-28 10:14:27.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3301
2025-08-28 10:14:27.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4642
2025-08-28 10:14:27.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:14:27.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:14:27.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-08-28 10:14:27.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-28 10:14:27.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.330
2025-08-28 10:14:27.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.464
2025-08-28 10:14:27.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:14:27.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:14:27.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:14:27.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:14:27.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:14:27.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:14:27.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:14:27.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:14:27.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:14:31.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:14:35.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:14:38.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:14:42.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:14:45.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:14:49.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:14:52.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:14:56.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:15:00.109 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:15:00.109 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 10:15:00.109 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 10:15:00.110 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:15:00.145 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.99 ms, Average inference time: 7.20 ms

2025-08-28 10:15:00.146 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:15:00.223 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:15:00.305 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch9
2025-08-28 10:15:03.400 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 9/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.999e-03, size: 448, ETA: 3:17:24
2025-08-28 10:15:06.685 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 9/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 4.3, cls_loss: 1.1, lr: 1.999e-03, size: 544, ETA: 3:17:30
2025-08-28 10:15:09.952 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 9/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 1.8, cls_loss: 0.7, lr: 1.999e-03, size: 576, ETA: 3:17:36
2025-08-28 10:15:13.242 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 9/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.999e-03, size: 512, ETA: 3:17:42
2025-08-28 10:15:16.451 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 9/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 1.1, lr: 1.999e-03, size: 480, ETA: 3:17:42
2025-08-28 10:15:19.733 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 9/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 6.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.9, lr: 1.999e-03, size: 288, ETA: 3:17:47
2025-08-28 10:15:21.130 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:15:27.441 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:15:29.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:15:31.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5474
2025-08-28 10:15:31.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4633
2025-08-28 10:15:31.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3583
2025-08-28 10:15:31.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4563
2025-08-28 10:15:31.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:15:31.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:15:31.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.547
2025-08-28 10:15:31.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.463
2025-08-28 10:15:31.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-08-28 10:15:31.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.456
2025-08-28 10:15:31.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:15:31.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:15:31.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:15:31.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:15:31.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:15:31.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:15:31.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:15:31.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:15:31.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:15:33.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:15:35.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:15:37.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:15:39.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:15:41.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:15:43.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:15:45.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:15:47.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:15:48.947 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:15:48.948 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 10:15:48.948 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 10:15:48.948 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:15:48.975 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.97 ms, Average inference time: 7.20 ms

2025-08-28 10:15:48.977 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:15:49.110 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:15:49.198 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch10
2025-08-28 10:15:52.450 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 10/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.1, lr: 1.999e-03, size: 256, ETA: 3:17:46
2025-08-28 10:15:55.900 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 10/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.2, lr: 1.999e-03, size: 512, ETA: 3:18:01
2025-08-28 10:15:59.187 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 10/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.999e-03, size: 384, ETA: 3:18:06
2025-08-28 10:16:02.444 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 10/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.999e-03, size: 256, ETA: 3:18:08
2025-08-28 10:16:05.660 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 10/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.2, lr: 1.999e-03, size: 512, ETA: 3:18:08
2025-08-28 10:16:08.941 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 10/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 8.5, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.0, lr: 1.999e-03, size: 256, ETA: 3:18:11
2025-08-28 10:16:10.381 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:16:16.677 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:16:19.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:16:21.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5588
2025-08-28 10:16:22.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5152
2025-08-28 10:16:22.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3524
2025-08-28 10:16:22.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4755
2025-08-28 10:16:22.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:16:22.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:16:22.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-08-28 10:16:22.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 10:16:22.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.352
2025-08-28 10:16:22.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.475
2025-08-28 10:16:22.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:16:22.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:16:22.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:16:22.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:16:22.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:16:22.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:16:22.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:16:22.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:16:22.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:16:24.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:16:26.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:16:29.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:16:31.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:16:34.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:16:36.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:16:38.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:16:41.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:16:43.471 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:16:43.471 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 10:16:43.471 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 10:16:43.472 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:16:43.497 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.96 ms, Average inference time: 7.17 ms

2025-08-28 10:16:43.504 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:16:43.580 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:16:43.663 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch11
2025-08-28 10:16:46.725 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 11/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.999e-03, size: 512, ETA: 3:18:02
2025-08-28 10:16:49.935 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 11/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.999e-03, size: 480, ETA: 3:18:01
2025-08-28 10:16:53.157 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 11/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.998e-03, size: 320, ETA: 3:18:01
2025-08-28 10:16:56.488 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 11/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.998e-03, size: 576, ETA: 3:18:07
2025-08-28 10:16:59.732 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 11/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.998e-03, size: 480, ETA: 3:18:07
2025-08-28 10:17:02.987 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 11/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.998e-03, size: 384, ETA: 3:18:08
2025-08-28 10:17:04.389 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:17:10.797 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:17:12.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:17:13.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5502
2025-08-28 10:17:13.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5021
2025-08-28 10:17:13.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3214
2025-08-28 10:17:13.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4579
2025-08-28 10:17:13.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:17:13.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:17:13.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.550
2025-08-28 10:17:13.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-08-28 10:17:13.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.321
2025-08-28 10:17:13.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.458
2025-08-28 10:17:13.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:17:13.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:17:13.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:17:13.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:17:13.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:17:13.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:17:13.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:17:13.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:17:13.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:17:15.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:17:16.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:17:18.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:17:19.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:17:21.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:17:22.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:17:23.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:17:25.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:17:26.709 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:17:26.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 10:17:26.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 10:17:26.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:17:26.719 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.96 ms, Average inference time: 7.19 ms

2025-08-28 10:17:26.721 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:17:26.807 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:17:26.943 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch12
2025-08-28 10:17:30.143 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 12/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.998e-03, size: 576, ETA: 3:18:05
2025-08-28 10:17:33.399 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 12/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 1.8, cls_loss: 0.8, lr: 1.998e-03, size: 256, ETA: 3:18:06
2025-08-28 10:17:36.664 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 12/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.2, lr: 1.998e-03, size: 416, ETA: 3:18:07
2025-08-28 10:17:39.864 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 12/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.998e-03, size: 416, ETA: 3:18:06
2025-08-28 10:17:43.072 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 12/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.998e-03, size: 448, ETA: 3:18:04
2025-08-28 10:17:46.408 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 12/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.998e-03, size: 512, ETA: 3:18:08
2025-08-28 10:17:47.849 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:17:54.076 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:17:56.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:17:57.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5783
2025-08-28 10:17:58.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5183
2025-08-28 10:17:58.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3264
2025-08-28 10:17:58.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4744
2025-08-28 10:17:58.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:17:58.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:17:58.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-08-28 10:17:58.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.518
2025-08-28 10:17:58.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.326
2025-08-28 10:17:58.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.474
2025-08-28 10:17:58.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:17:58.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:17:58.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:17:58.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:17:58.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:17:58.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:17:58.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:17:58.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:17:58.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:18:00.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:18:02.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:18:04.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:18:06.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:18:08.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:18:09.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:18:11.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:18:13.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:18:15.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:18:15.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 10:18:15.667 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 10:18:15.667 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:18:15.693 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.95 ms, Average inference time: 7.19 ms

2025-08-28 10:18:15.695 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:18:15.781 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:18:15.864 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch13
2025-08-28 10:18:19.017 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 13/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.998e-03, size: 320, ETA: 3:18:03
2025-08-28 10:18:22.279 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 13/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.998e-03, size: 448, ETA: 3:18:03
2025-08-28 10:18:25.735 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 13/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.998e-03, size: 480, ETA: 3:18:13
2025-08-28 10:18:28.959 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 13/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.998e-03, size: 288, ETA: 3:18:12
2025-08-28 10:18:32.222 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 13/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.998e-03, size: 512, ETA: 3:18:13
2025-08-28 10:18:35.486 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 13/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.998e-03, size: 320, ETA: 3:18:13
2025-08-28 10:18:36.908 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:18:43.177 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:18:45.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:18:46.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5397
2025-08-28 10:18:47.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4992
2025-08-28 10:18:47.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3449
2025-08-28 10:18:47.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4613
2025-08-28 10:18:47.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:18:47.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:18:47.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.540
2025-08-28 10:18:47.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-08-28 10:18:47.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.345
2025-08-28 10:18:47.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.461
2025-08-28 10:18:47.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:18:47.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:18:47.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:18:47.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:18:47.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:18:47.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:18:47.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:18:47.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:18:47.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:18:48.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:18:50.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:18:52.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:18:54.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:18:55.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:18:57.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:18:59.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:19:00.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:19:02.784 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:19:02.784 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 10:19:02.784 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 10:19:02.784 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:19:02.811 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.94 ms, Average inference time: 7.14 ms

2025-08-28 10:19:02.813 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:19:02.903 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:19:02.993 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch14
2025-08-28 10:19:06.157 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 14/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.998e-03, size: 512, ETA: 3:18:08
2025-08-28 10:19:09.529 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 14/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.166s, data_time: 0.007s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.998e-03, size: 384, ETA: 3:18:12
2025-08-28 10:19:12.688 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 14/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.998e-03, size: 256, ETA: 3:18:07
2025-08-28 10:19:15.955 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 14/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.997e-03, size: 384, ETA: 3:18:07
2025-08-28 10:19:19.263 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 14/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.997e-03, size: 544, ETA: 3:18:10
2025-08-28 10:19:22.675 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 14/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.169s, data_time: 0.003s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.997e-03, size: 288, ETA: 3:18:17
2025-08-28 10:19:24.172 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:19:30.479 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:19:32.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:19:34.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5659
2025-08-28 10:19:35.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4902
2025-08-28 10:19:35.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3015
2025-08-28 10:19:35.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4526
2025-08-28 10:19:35.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:19:35.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:19:35.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.566
2025-08-28 10:19:35.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-28 10:19:35.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.302
2025-08-28 10:19:35.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.453
2025-08-28 10:19:35.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:19:35.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:19:35.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:19:35.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:19:35.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:19:35.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:19:35.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:19:35.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:19:35.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:19:37.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:19:39.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:19:41.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:19:43.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:19:45.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:19:47.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:19:49.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:19:51.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:19:53.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:19:53.032 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 10:19:53.032 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 10:19:53.032 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:19:53.057 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 1.00 ms, Average inference time: 7.15 ms

2025-08-28 10:19:53.060 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:19:53.145 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:19:53.228 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch15
2025-08-28 10:19:56.284 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 15/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.997e-03, size: 416, ETA: 3:18:09
2025-08-28 10:19:59.729 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 15/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.997e-03, size: 416, ETA: 3:18:16
2025-08-28 10:20:02.885 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 15/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.997e-03, size: 448, ETA: 3:18:12
2025-08-28 10:20:06.342 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 15/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.997e-03, size: 320, ETA: 3:18:19
2025-08-28 10:20:09.563 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 15/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.997e-03, size: 288, ETA: 3:18:17
2025-08-28 10:20:12.953 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 15/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.997e-03, size: 320, ETA: 3:18:21
2025-08-28 10:20:14.335 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:20:20.646 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:20:23.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:20:24.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5490
2025-08-28 10:20:25.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5099
2025-08-28 10:20:25.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3082
2025-08-28 10:20:25.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4557
2025-08-28 10:20:25.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:20:25.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:20:25.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.549
2025-08-28 10:20:25.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.510
2025-08-28 10:20:25.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.308
2025-08-28 10:20:25.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.456
2025-08-28 10:20:25.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:20:25.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:20:25.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:20:25.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:20:25.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:20:25.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:20:25.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:20:25.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:20:25.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:20:27.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:20:29.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:20:31.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:20:33.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:20:35.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:20:37.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:20:40.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:20:42.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:20:44.362 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:20:44.362 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 10:20:44.362 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 10:20:44.363 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:20:44.394 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 1.00 ms, Average inference time: 7.19 ms

2025-08-28 10:20:44.395 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:20:44.477 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:20:44.567 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch16
2025-08-28 10:20:47.712 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 16/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.997e-03, size: 256, ETA: 3:18:12
2025-08-28 10:20:50.988 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 16/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.997e-03, size: 544, ETA: 3:18:13
2025-08-28 10:20:54.333 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 16/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.997e-03, size: 416, ETA: 3:18:15
2025-08-28 10:20:57.524 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 16/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.997e-03, size: 288, ETA: 3:18:12
2025-08-28 10:21:00.651 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 16/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.997e-03, size: 256, ETA: 3:18:06
2025-08-28 10:21:03.792 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 16/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.997e-03, size: 288, ETA: 3:18:02
2025-08-28 10:21:05.172 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:21:11.579 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:21:13.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:21:14.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5512
2025-08-28 10:21:14.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4876
2025-08-28 10:21:14.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3065
2025-08-28 10:21:14.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4484
2025-08-28 10:21:14.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:21:14.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:21:14.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-08-28 10:21:14.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-08-28 10:21:14.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.307
2025-08-28 10:21:14.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.448
2025-08-28 10:21:14.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:21:14.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:21:14.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:21:14.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:21:14.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:21:14.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:21:14.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:21:14.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:21:14.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:21:16.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:21:17.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:21:19.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:21:20.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:21:22.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:21:23.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:21:25.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:21:26.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:21:27.962 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:21:27.962 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 10:21:27.962 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 10:21:27.962 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:21:27.972 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.98 ms, Average inference time: 7.17 ms

2025-08-28 10:21:27.974 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:21:28.054 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:21:28.139 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch17
2025-08-28 10:21:31.295 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 17/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.996e-03, size: 384, ETA: 3:17:53
2025-08-28 10:21:34.602 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 17/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.996e-03, size: 256, ETA: 3:17:54
2025-08-28 10:21:37.912 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 17/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.996e-03, size: 416, ETA: 3:17:55
2025-08-28 10:21:41.087 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 17/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.996e-03, size: 288, ETA: 3:17:51
2025-08-28 10:21:44.399 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 17/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.996e-03, size: 544, ETA: 3:17:51
2025-08-28 10:21:47.800 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 17/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.167s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.996e-03, size: 288, ETA: 3:17:55
2025-08-28 10:21:49.232 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:21:55.536 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:21:57.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:21:58.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5371
2025-08-28 10:21:59.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4432
2025-08-28 10:21:59.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2968
2025-08-28 10:21:59.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4257
2025-08-28 10:21:59.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:21:59.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:21:59.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-08-28 10:21:59.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.443
2025-08-28 10:21:59.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.297
2025-08-28 10:21:59.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.426
2025-08-28 10:21:59.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:21:59.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:21:59.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:21:59.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:21:59.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:21:59.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:21:59.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:21:59.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:21:59.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:22:01.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:22:02.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:22:04.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:22:05.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:22:07.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:22:09.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:22:11.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:22:12.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:22:14.315 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:22:14.315 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 10:22:14.315 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 10:22:14.315 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:22:14.340 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.98 ms, Average inference time: 7.12 ms

2025-08-28 10:22:14.342 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:22:14.434 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:22:14.522 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch18
2025-08-28 10:22:17.578 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 18/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.996e-03, size: 512, ETA: 3:17:45
2025-08-28 10:22:21.013 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 18/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.996e-03, size: 544, ETA: 3:17:49
2025-08-28 10:22:24.299 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 18/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.996e-03, size: 576, ETA: 3:17:49
2025-08-28 10:22:27.579 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 18/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.0Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.996e-03, size: 320, ETA: 3:17:49
2025-08-28 10:22:30.795 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 18/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.996e-03, size: 448, ETA: 3:17:47
2025-08-28 10:22:34.060 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 18/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.162s, data_time: 0.006s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.996e-03, size: 480, ETA: 3:17:46
2025-08-28 10:22:35.556 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:22:41.762 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:22:43.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:22:45.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5748
2025-08-28 10:22:45.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4936
2025-08-28 10:22:45.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3284
2025-08-28 10:22:45.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4656
2025-08-28 10:22:45.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:22:45.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:22:45.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.575
2025-08-28 10:22:45.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-08-28 10:22:45.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.328
2025-08-28 10:22:45.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.466
2025-08-28 10:22:45.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:22:45.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:22:45.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:22:45.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:22:45.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:22:45.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:22:45.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:22:45.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:22:45.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:22:47.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:22:48.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:22:50.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:22:52.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:22:53.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:22:55.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:22:56.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:22:58.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:23:00.074 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:23:00.075 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 10:23:00.075 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 10:23:00.075 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:23:00.103 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.97 ms, Average inference time: 7.14 ms

2025-08-28 10:23:00.104 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:23:00.233 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:23:00.314 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch19
2025-08-28 10:23:03.354 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 19/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.995e-03, size: 544, ETA: 3:17:38
2025-08-28 10:23:06.654 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 19/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 1.8, cls_loss: 0.8, lr: 1.995e-03, size: 320, ETA: 3:17:37
2025-08-28 10:23:09.876 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 19/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.995e-03, size: 352, ETA: 3:17:34
2025-08-28 10:23:13.026 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 19/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.995e-03, size: 416, ETA: 3:17:30
2025-08-28 10:23:16.211 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 19/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.995e-03, size: 544, ETA: 3:17:26
2025-08-28 10:23:19.467 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 19/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 7.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.995e-03, size: 448, ETA: 3:17:24
2025-08-28 10:23:20.963 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:23:27.318 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:23:29.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:23:30.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5595
2025-08-28 10:23:31.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3832
2025-08-28 10:23:31.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2884
2025-08-28 10:23:31.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4104
2025-08-28 10:23:31.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:23:31.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:23:31.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.560
2025-08-28 10:23:31.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-28 10:23:31.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.288
2025-08-28 10:23:31.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.410
2025-08-28 10:23:31.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:23:31.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:23:31.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:23:31.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:23:31.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:23:31.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:23:31.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:23:31.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:23:31.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:23:32.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:23:34.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:23:36.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:23:38.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:23:39.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:23:41.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:23:43.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:23:44.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:23:46.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:23:46.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-28 10:23:46.665 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-28 10:23:46.665 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:23:46.713 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.95 ms, Average inference time: 7.14 ms

2025-08-28 10:23:46.716 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:23:46.801 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:23:46.888 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch20
2025-08-28 10:23:49.984 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 20/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.995e-03, size: 544, ETA: 3:17:18
2025-08-28 10:23:53.217 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 20/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.8, lr: 1.995e-03, size: 288, ETA: 3:17:15
2025-08-28 10:23:56.355 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 20/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.995e-03, size: 544, ETA: 3:17:10
2025-08-28 10:23:59.748 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 20/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.995e-03, size: 384, ETA: 3:17:13
2025-08-28 10:24:03.023 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 20/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.995e-03, size: 320, ETA: 3:17:12
2025-08-28 10:24:06.230 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 20/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.995e-03, size: 352, ETA: 3:17:08
2025-08-28 10:24:07.631 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:24:13.815 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:24:17.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:24:20.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5415
2025-08-28 10:24:20.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4551
2025-08-28 10:24:20.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2978
2025-08-28 10:24:20.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4315
2025-08-28 10:24:20.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:24:20.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:24:20.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-08-28 10:24:20.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-08-28 10:24:20.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.298
2025-08-28 10:24:20.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.431
2025-08-28 10:24:20.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:24:20.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:24:20.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:24:20.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:24:20.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:24:20.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:24:20.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:24:20.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:24:20.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:24:23.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:24:27.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:24:30.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:24:33.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:24:36.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:24:39.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:24:43.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:24:46.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:24:49.240 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:24:49.241 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 10:24:49.241 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 10:24:49.241 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:24:49.270 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.96 ms, Average inference time: 7.12 ms

2025-08-28 10:24:49.272 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:24:49.410 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:24:49.492 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch21
2025-08-28 10:24:52.555 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 21/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.994e-03, size: 448, ETA: 3:16:59
2025-08-28 10:24:55.928 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 21/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.994e-03, size: 576, ETA: 3:17:00
2025-08-28 10:24:59.146 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 21/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.994e-03, size: 480, ETA: 3:16:58
2025-08-28 10:25:02.325 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 21/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.994e-03, size: 448, ETA: 3:16:54
2025-08-28 10:25:05.499 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 21/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.994e-03, size: 320, ETA: 3:16:50
2025-08-28 10:25:08.767 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 21/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.994e-03, size: 480, ETA: 3:16:48
2025-08-28 10:25:10.304 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:25:16.507 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:25:18.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:25:19.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4982
2025-08-28 10:25:20.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4309
2025-08-28 10:25:20.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3061
2025-08-28 10:25:20.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4117
2025-08-28 10:25:20.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:25:20.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:25:20.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-28 10:25:20.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-08-28 10:25:20.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.306
2025-08-28 10:25:20.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.412
2025-08-28 10:25:20.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:25:20.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:25:20.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:25:20.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:25:20.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:25:20.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:25:20.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:25:20.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:25:20.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:25:21.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:25:23.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:25:25.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:25:26.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:25:28.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:25:30.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:25:31.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:25:33.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:25:35.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:25:35.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 10:25:35.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-28 10:25:35.085 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:25:35.117 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.96 ms, Average inference time: 7.15 ms

2025-08-28 10:25:35.118 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:25:35.233 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:25:35.388 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch22
2025-08-28 10:25:38.507 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 22/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.994e-03, size: 352, ETA: 3:16:45
2025-08-28 10:25:41.867 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 22/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.994e-03, size: 576, ETA: 3:16:45
2025-08-28 10:25:45.035 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 22/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.994e-03, size: 352, ETA: 3:16:42
2025-08-28 10:25:48.184 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 22/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.994e-03, size: 384, ETA: 3:16:37
2025-08-28 10:25:51.452 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 22/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.994e-03, size: 416, ETA: 3:16:35
2025-08-28 10:25:54.924 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 22/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.172s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.9, lr: 1.993e-03, size: 576, ETA: 3:16:39
2025-08-28 10:25:56.381 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:26:02.734 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:26:04.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:26:06.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5601
2025-08-28 10:26:06.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4677
2025-08-28 10:26:06.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3324
2025-08-28 10:26:06.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4534
2025-08-28 10:26:06.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:26:06.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:26:06.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.560
2025-08-28 10:26:06.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-08-28 10:26:06.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.332
2025-08-28 10:26:06.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.453
2025-08-28 10:26:06.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:26:06.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:26:06.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:26:06.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:26:06.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:26:06.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:26:06.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:26:06.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:26:06.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:26:08.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:26:09.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:26:11.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:26:13.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:26:14.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:26:16.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:26:17.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:26:19.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:26:21.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:26:21.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 10:26:21.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 10:26:21.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:26:21.315 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.96 ms, Average inference time: 7.26 ms

2025-08-28 10:26:21.316 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:26:21.392 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:26:21.473 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch23
2025-08-28 10:26:24.740 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 23/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.993e-03, size: 512, ETA: 3:16:37
2025-08-28 10:26:28.151 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 23/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.993e-03, size: 576, ETA: 3:16:38
2025-08-28 10:26:31.414 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 23/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.993e-03, size: 320, ETA: 3:16:37
2025-08-28 10:26:34.592 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 23/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.993e-03, size: 384, ETA: 3:16:32
2025-08-28 10:26:37.730 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 23/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.2, lr: 1.993e-03, size: 416, ETA: 3:16:27
2025-08-28 10:26:41.035 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 23/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.993e-03, size: 416, ETA: 3:16:26
2025-08-28 10:26:42.555 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:26:48.803 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:26:50.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:26:51.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5157
2025-08-28 10:26:52.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4596
2025-08-28 10:26:52.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2680
2025-08-28 10:26:52.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4144
2025-08-28 10:26:52.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:26:52.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:26:52.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-08-28 10:26:52.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-08-28 10:26:52.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.268
2025-08-28 10:26:52.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.414
2025-08-28 10:26:52.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:26:52.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:26:52.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:26:52.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:26:52.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:26:52.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:26:52.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:26:52.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:26:52.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:26:53.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:26:55.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:26:56.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:26:58.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:26:59.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:27:01.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:27:02.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:27:04.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:27:05.994 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:27:05.995 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 10:27:05.995 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-28 10:27:05.995 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:27:06.009 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.95 ms, Average inference time: 7.15 ms

2025-08-28 10:27:06.010 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:27:06.088 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:27:06.180 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch24
2025-08-28 10:27:09.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 24/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.993e-03, size: 320, ETA: 3:16:25
2025-08-28 10:27:12.630 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 24/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.993e-03, size: 448, ETA: 3:16:21
2025-08-28 10:27:15.971 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 24/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.992e-03, size: 288, ETA: 3:16:21
2025-08-28 10:27:19.233 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 24/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.992e-03, size: 480, ETA: 3:16:19
2025-08-28 10:27:22.488 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 24/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.992e-03, size: 352, ETA: 3:16:17
2025-08-28 10:27:25.863 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 24/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.992e-03, size: 320, ETA: 3:16:17
2025-08-28 10:27:27.442 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:27:33.519 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:27:35.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:27:37.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5551
2025-08-28 10:27:37.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5060
2025-08-28 10:27:37.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3294
2025-08-28 10:27:37.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4635
2025-08-28 10:27:37.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:27:37.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:27:37.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.555
2025-08-28 10:27:37.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-08-28 10:27:37.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.329
2025-08-28 10:27:37.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.463
2025-08-28 10:27:37.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:27:37.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:27:37.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:27:37.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:27:37.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:27:37.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:27:37.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:27:37.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:27:37.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:27:39.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:27:41.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:27:43.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:27:45.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:27:46.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:27:48.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:27:50.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:27:52.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:27:54.410 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:27:54.411 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 10:27:54.411 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 10:27:54.411 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:27:54.439 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.10 ms, Average NMS time: 1.02 ms, Average inference time: 7.12 ms

2025-08-28 10:27:54.440 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:27:54.519 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:27:54.600 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch25
2025-08-28 10:27:57.795 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 25/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.992e-03, size: 576, ETA: 3:16:15
2025-08-28 10:28:01.203 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 25/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.992e-03, size: 256, ETA: 3:16:16
2025-08-28 10:28:04.399 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 25/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.992e-03, size: 320, ETA: 3:16:12
2025-08-28 10:28:07.736 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 25/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.992e-03, size: 352, ETA: 3:16:12
2025-08-28 10:28:10.829 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 25/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.992e-03, size: 256, ETA: 3:16:05
2025-08-28 10:28:13.999 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 25/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.991e-03, size: 288, ETA: 3:16:01
2025-08-28 10:28:15.397 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:28:21.724 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:28:25.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:28:28.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5404
2025-08-28 10:28:28.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4476
2025-08-28 10:28:28.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2982
2025-08-28 10:28:28.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4287
2025-08-28 10:28:28.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:28:28.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:28:28.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.540
2025-08-28 10:28:28.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.448
2025-08-28 10:28:28.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.298
2025-08-28 10:28:28.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.429
2025-08-28 10:28:28.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:28:28.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:28:28.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:28:28.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:28:28.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:28:28.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:28:28.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:28:28.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:28:28.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:28:31.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:28:34.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:28:37.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:28:40.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:28:43.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:28:46.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:28:49.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:28:52.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:28:55.405 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:28:55.405 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 10:28:55.405 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 10:28:55.405 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:28:55.431 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.97 ms, Average inference time: 7.19 ms

2025-08-28 10:28:55.432 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:28:55.548 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:28:55.651 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch26
2025-08-28 10:28:58.654 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 26/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.991e-03, size: 544, ETA: 3:15:51
2025-08-28 10:29:01.954 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 26/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.991e-03, size: 352, ETA: 3:15:49
2025-08-28 10:29:05.154 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 26/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.991e-03, size: 448, ETA: 3:15:46
2025-08-28 10:29:08.380 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 26/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.991e-03, size: 384, ETA: 3:15:43
2025-08-28 10:29:11.669 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 26/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.991e-03, size: 544, ETA: 3:15:41
2025-08-28 10:29:14.933 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 26/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.991e-03, size: 576, ETA: 3:15:39
2025-08-28 10:29:16.508 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:29:22.815 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:29:25.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:29:27.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5434
2025-08-28 10:29:27.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4774
2025-08-28 10:29:27.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3373
2025-08-28 10:29:27.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4527
2025-08-28 10:29:27.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:29:27.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:29:27.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-08-28 10:29:27.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.477
2025-08-28 10:29:27.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.337
2025-08-28 10:29:27.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.453
2025-08-28 10:29:27.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:29:27.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:29:27.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:29:27.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:29:27.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:29:27.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:29:27.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:29:27.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:29:27.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:29:29.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:29:32.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:29:34.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:29:36.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:29:38.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:29:40.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:29:42.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:29:45.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:29:47.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:29:47.223 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 10:29:47.223 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 10:29:47.223 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:29:47.255 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.94 ms, Average inference time: 7.09 ms

2025-08-28 10:29:47.257 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:29:47.338 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:29:47.420 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch27
2025-08-28 10:29:50.513 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 27/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.991e-03, size: 544, ETA: 3:15:34
2025-08-28 10:29:53.827 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 27/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.991e-03, size: 256, ETA: 3:15:34
2025-08-28 10:29:56.985 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 27/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.990e-03, size: 576, ETA: 3:15:29
2025-08-28 10:30:00.328 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 27/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.990e-03, size: 544, ETA: 3:15:29
2025-08-28 10:30:03.667 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 27/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.990e-03, size: 576, ETA: 3:15:28
2025-08-28 10:30:07.036 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 27/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.990e-03, size: 320, ETA: 3:15:28
2025-08-28 10:30:08.483 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:30:14.720 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:30:17.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:30:19.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5326
2025-08-28 10:30:19.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4536
2025-08-28 10:30:19.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3160
2025-08-28 10:30:19.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4340
2025-08-28 10:30:19.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:30:19.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:30:19.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-08-28 10:30:19.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.454
2025-08-28 10:30:19.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-28 10:30:19.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-08-28 10:30:19.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:30:19.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:30:19.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:30:19.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:30:19.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:30:19.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:30:19.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:30:19.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:30:19.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:30:22.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:30:24.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:30:26.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:30:29.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:30:31.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:30:33.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:30:36.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:30:38.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:30:40.926 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:30:40.927 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 10:30:40.927 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 10:30:40.927 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:30:40.954 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.07 ms, Average NMS time: 0.99 ms, Average inference time: 7.06 ms

2025-08-28 10:30:40.955 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:30:41.101 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:30:41.188 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch28
2025-08-28 10:30:44.216 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 28/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.990e-03, size: 416, ETA: 3:15:20
2025-08-28 10:30:47.531 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 28/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.990e-03, size: 576, ETA: 3:15:18
2025-08-28 10:30:50.950 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 28/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.990e-03, size: 512, ETA: 3:15:19
2025-08-28 10:30:54.247 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 28/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.990e-03, size: 256, ETA: 3:15:17
2025-08-28 10:30:57.340 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 28/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.989e-03, size: 288, ETA: 3:15:12
2025-08-28 10:31:00.482 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 28/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.989e-03, size: 352, ETA: 3:15:07
2025-08-28 10:31:01.886 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:31:08.014 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:31:10.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:31:11.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5490
2025-08-28 10:31:12.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4657
2025-08-28 10:31:12.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3335
2025-08-28 10:31:12.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4494
2025-08-28 10:31:12.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:31:12.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:31:12.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.549
2025-08-28 10:31:12.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-08-28 10:31:12.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-08-28 10:31:12.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.449
2025-08-28 10:31:12.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:31:12.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:31:12.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:31:12.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:31:12.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:31:12.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:31:12.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:31:12.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:31:12.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:31:14.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:31:16.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:31:17.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:31:19.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:31:21.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:31:23.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:31:25.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:31:27.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:31:29.189 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:31:29.190 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 10:31:29.190 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 10:31:29.190 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:31:29.218 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.93 ms, Average inference time: 7.18 ms

2025-08-28 10:31:29.219 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:31:29.302 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:31:29.385 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch29
2025-08-28 10:31:32.502 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 29/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.989e-03, size: 480, ETA: 3:14:59
2025-08-28 10:31:35.766 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 29/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.989e-03, size: 352, ETA: 3:14:57
2025-08-28 10:31:39.008 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 29/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.989e-03, size: 576, ETA: 3:14:54
2025-08-28 10:31:42.405 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 29/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.989e-03, size: 352, ETA: 3:14:55
2025-08-28 10:31:45.619 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 29/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.989e-03, size: 576, ETA: 3:14:52
2025-08-28 10:31:49.087 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 29/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.989e-03, size: 288, ETA: 3:14:53
2025-08-28 10:31:50.541 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:31:56.981 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:32:00.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:32:03.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4930
2025-08-28 10:32:03.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4354
2025-08-28 10:32:03.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2987
2025-08-28 10:32:03.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4090
2025-08-28 10:32:03.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:32:03.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:32:03.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-08-28 10:32:03.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.435
2025-08-28 10:32:03.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.299
2025-08-28 10:32:03.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.409
2025-08-28 10:32:03.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:32:03.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:32:03.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:32:03.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:32:03.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:32:03.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:32:03.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:32:03.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:32:03.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:32:06.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:32:10.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:32:13.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:32:16.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:32:19.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:32:22.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:32:25.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:32:28.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:32:31.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:32:31.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 10:32:31.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-28 10:32:31.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:32:31.456 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 1.02 ms, Average inference time: 7.20 ms

2025-08-28 10:32:31.457 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:32:31.543 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:32:31.633 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch30
2025-08-28 10:32:34.728 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 30/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.988e-03, size: 448, ETA: 3:14:46
2025-08-28 10:32:37.983 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 30/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.988e-03, size: 384, ETA: 3:14:44
2025-08-28 10:32:41.364 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 30/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.988e-03, size: 448, ETA: 3:14:43
2025-08-28 10:32:44.539 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 30/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.988e-03, size: 288, ETA: 3:14:39
2025-08-28 10:32:47.850 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 30/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.988e-03, size: 384, ETA: 3:14:37
2025-08-28 10:32:51.155 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 30/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.988e-03, size: 576, ETA: 3:14:36
2025-08-28 10:32:52.679 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:32:58.869 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:33:00.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:33:02.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5537
2025-08-28 10:33:02.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4811
2025-08-28 10:33:02.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3215
2025-08-28 10:33:02.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4521
2025-08-28 10:33:02.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:33:02.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:33:02.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.554
2025-08-28 10:33:02.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-28 10:33:02.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.322
2025-08-28 10:33:02.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.452
2025-08-28 10:33:02.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:33:02.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:33:02.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:33:02.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:33:02.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:33:02.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:33:02.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:33:02.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:33:02.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:33:03.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:33:05.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:33:07.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:33:08.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:33:10.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:33:11.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:33:13.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:33:14.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:33:16.591 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:33:16.591 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 10:33:16.591 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 10:33:16.591 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:33:16.616 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.98 ms, Average inference time: 7.09 ms

2025-08-28 10:33:16.617 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:33:16.688 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:33:16.790 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch31
2025-08-28 10:33:19.923 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 31/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.988e-03, size: 256, ETA: 3:14:31
2025-08-28 10:33:23.076 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 31/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.987e-03, size: 320, ETA: 3:14:27
2025-08-28 10:33:26.323 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 31/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 1.987e-03, size: 256, ETA: 3:14:25
2025-08-28 10:33:29.548 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 31/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.987e-03, size: 512, ETA: 3:14:21
2025-08-28 10:33:32.751 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 31/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.987e-03, size: 416, ETA: 3:14:18
2025-08-28 10:33:35.917 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 31/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.987e-03, size: 512, ETA: 3:14:14
2025-08-28 10:33:37.360 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:33:43.687 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:33:47.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:33:49.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4909
2025-08-28 10:33:50.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4139
2025-08-28 10:33:50.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2931
2025-08-28 10:33:50.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3993
2025-08-28 10:33:50.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:33:50.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:33:50.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-08-28 10:33:50.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-08-28 10:33:50.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.293
2025-08-28 10:33:50.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.399
2025-08-28 10:33:50.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:33:50.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:33:50.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:33:50.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:33:50.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:33:50.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:33:50.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:33:50.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:33:50.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:33:53.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:33:56.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:33:59.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:34:03.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:34:06.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:34:09.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:34:12.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:34:15.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:34:18.625 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:34:18.625 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-28 10:34:18.625 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 10:34:18.625 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:34:18.650 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.98 ms, Average inference time: 7.24 ms

2025-08-28 10:34:18.651 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:34:18.767 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:34:18.857 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch32
2025-08-28 10:34:21.951 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 32/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.987e-03, size: 448, ETA: 3:14:07
2025-08-28 10:34:25.177 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 32/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.4, lr: 1.987e-03, size: 320, ETA: 3:14:04
2025-08-28 10:34:28.321 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 32/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.9, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.986e-03, size: 288, ETA: 3:14:00
2025-08-28 10:34:31.761 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 32/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.986e-03, size: 512, ETA: 3:14:01
2025-08-28 10:34:34.952 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 32/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.986e-03, size: 480, ETA: 3:13:57
2025-08-28 10:34:38.212 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 32/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.986e-03, size: 416, ETA: 3:13:54
2025-08-28 10:34:39.660 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:34:46.064 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:34:48.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:34:49.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5575
2025-08-28 10:34:49.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4694
2025-08-28 10:34:49.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2917
2025-08-28 10:34:49.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4396
2025-08-28 10:34:49.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:34:49.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:34:49.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.558
2025-08-28 10:34:49.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-08-28 10:34:49.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.292
2025-08-28 10:34:49.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.440
2025-08-28 10:34:49.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:34:49.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:34:49.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:34:49.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:34:49.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:34:49.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:34:49.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:34:49.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:34:49.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:34:51.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:34:52.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:34:54.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:34:56.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:34:57.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:34:59.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:35:00.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:35:02.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:35:03.794 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:35:03.795 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 10:35:03.795 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 10:35:03.795 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:35:03.819 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.98 ms, Average inference time: 7.13 ms

2025-08-28 10:35:03.821 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:35:03.901 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:35:03.982 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch33
2025-08-28 10:35:07.094 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 33/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.986e-03, size: 544, ETA: 3:13:48
2025-08-28 10:35:10.352 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 33/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.986e-03, size: 416, ETA: 3:13:45
2025-08-28 10:35:13.530 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 33/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.986e-03, size: 448, ETA: 3:13:41
2025-08-28 10:35:16.629 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 33/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.985e-03, size: 384, ETA: 3:13:36
2025-08-28 10:35:19.753 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 33/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.985e-03, size: 256, ETA: 3:13:31
2025-08-28 10:35:23.006 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 33/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.985e-03, size: 480, ETA: 3:13:29
2025-08-28 10:35:24.479 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:35:30.779 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:35:32.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:35:33.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4793
2025-08-28 10:35:33.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4768
2025-08-28 10:35:33.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3087
2025-08-28 10:35:33.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4216
2025-08-28 10:35:33.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:35:33.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:35:33.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-08-28 10:35:33.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.477
2025-08-28 10:35:33.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.309
2025-08-28 10:35:33.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.422
2025-08-28 10:35:33.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:35:33.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:35:33.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:35:33.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:35:33.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:35:33.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:35:33.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:35:33.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:35:33.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:35:34.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:35:36.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:35:37.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:35:38.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:35:39.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:35:40.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:35:42.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:35:43.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:35:44.567 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:35:44.568 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 10:35:44.568 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 10:35:44.568 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:35:44.576 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.10 ms, Average NMS time: 0.98 ms, Average inference time: 7.08 ms

2025-08-28 10:35:44.577 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:35:44.657 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:35:44.743 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch34
2025-08-28 10:35:47.794 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 34/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.985e-03, size: 256, ETA: 3:13:22
2025-08-28 10:35:51.025 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 34/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.985e-03, size: 512, ETA: 3:13:19
2025-08-28 10:35:54.165 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 34/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.985e-03, size: 384, ETA: 3:13:14
2025-08-28 10:35:57.390 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 34/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.985e-03, size: 320, ETA: 3:13:11
2025-08-28 10:36:00.677 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 34/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.984e-03, size: 544, ETA: 3:13:09
2025-08-28 10:36:04.181 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 34/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.173s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.984e-03, size: 576, ETA: 3:13:10
2025-08-28 10:36:05.677 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:36:11.895 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:36:13.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:36:15.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5442
2025-08-28 10:36:15.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4423
2025-08-28 10:36:15.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2923
2025-08-28 10:36:15.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4263
2025-08-28 10:36:15.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:36:15.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:36:15.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.544
2025-08-28 10:36:15.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.442
2025-08-28 10:36:15.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.292
2025-08-28 10:36:15.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.426
2025-08-28 10:36:15.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:36:15.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:36:15.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:36:15.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:36:15.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:36:15.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:36:15.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:36:15.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:36:15.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:36:17.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:36:18.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:36:20.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:36:21.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:36:23.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:36:24.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:36:26.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:36:28.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:36:29.606 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:36:29.606 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 10:36:29.606 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 10:36:29.607 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:36:29.630 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.95 ms, Average inference time: 7.22 ms

2025-08-28 10:36:29.632 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:36:29.709 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:36:29.791 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch35
2025-08-28 10:36:33.030 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 35/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 1.984e-03, size: 480, ETA: 3:13:07
2025-08-28 10:36:36.203 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 35/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.984e-03, size: 256, ETA: 3:13:03
2025-08-28 10:36:39.571 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 35/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.984e-03, size: 256, ETA: 3:13:03
2025-08-28 10:36:42.887 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 35/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.984e-03, size: 384, ETA: 3:13:01
2025-08-28 10:36:46.117 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 35/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.983e-03, size: 416, ETA: 3:12:58
2025-08-28 10:36:49.438 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 35/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.983e-03, size: 320, ETA: 3:12:56
2025-08-28 10:36:50.876 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:36:57.188 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:37:01.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:37:03.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5384
2025-08-28 10:37:03.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4297
2025-08-28 10:37:04.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2827
2025-08-28 10:37:04.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4170
2025-08-28 10:37:04.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:37:04.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:37:04.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.538
2025-08-28 10:37:04.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.430
2025-08-28 10:37:04.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.283
2025-08-28 10:37:04.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.417
2025-08-28 10:37:04.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:37:04.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:37:04.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:37:04.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:37:04.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:37:04.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:37:04.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:37:04.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:37:04.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:37:07.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:37:10.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:37:13.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:37:16.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:37:19.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:37:22.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:37:25.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:37:28.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:37:31.055 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:37:31.056 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 10:37:31.056 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 10:37:31.056 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:37:31.082 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 1.01 ms, Average inference time: 7.32 ms

2025-08-28 10:37:31.083 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:37:31.164 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:37:31.251 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch36
2025-08-28 10:37:34.386 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 36/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.7, lr: 1.983e-03, size: 480, ETA: 3:12:50
2025-08-28 10:37:37.576 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 36/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.1, lr: 1.983e-03, size: 416, ETA: 3:12:46
2025-08-28 10:37:40.898 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 36/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.9, lr: 1.983e-03, size: 544, ETA: 3:12:45
2025-08-28 10:37:44.077 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 36/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.983e-03, size: 256, ETA: 3:12:41
2025-08-28 10:37:47.294 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 36/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.983e-03, size: 448, ETA: 3:12:38
2025-08-28 10:37:50.587 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 36/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.982e-03, size: 544, ETA: 3:12:36
2025-08-28 10:37:52.116 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:37:58.386 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:38:02.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:38:06.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5309
2025-08-28 10:38:06.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4490
2025-08-28 10:38:06.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3053
2025-08-28 10:38:06.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4284
2025-08-28 10:38:06.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:38:06.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:38:06.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-08-28 10:38:06.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-08-28 10:38:06.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.305
2025-08-28 10:38:06.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.428
2025-08-28 10:38:06.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:38:06.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:38:06.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:38:06.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:38:06.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:38:06.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:38:06.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:38:06.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:38:06.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:38:10.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:38:14.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:38:18.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:38:22.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:38:25.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:38:29.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:38:33.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:38:37.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:38:40.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:38:40.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 10:38:40.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 10:38:40.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:38:41.008 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.97 ms, Average inference time: 7.23 ms

2025-08-28 10:38:41.009 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:38:41.124 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:38:41.207 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch37
2025-08-28 10:38:44.282 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 37/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.982e-03, size: 512, ETA: 3:12:30
2025-08-28 10:38:47.557 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 37/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.982e-03, size: 544, ETA: 3:12:28
2025-08-28 10:38:50.924 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 37/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.982e-03, size: 448, ETA: 3:12:27
2025-08-28 10:38:54.107 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 37/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.982e-03, size: 384, ETA: 3:12:23
2025-08-28 10:38:57.444 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 37/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.982e-03, size: 384, ETA: 3:12:22
2025-08-28 10:39:00.690 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 37/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.981e-03, size: 320, ETA: 3:12:19
2025-08-28 10:39:02.120 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:39:08.512 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:39:11.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:39:12.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4711
2025-08-28 10:39:12.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3799
2025-08-28 10:39:13.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1878
2025-08-28 10:39:13.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3462
2025-08-28 10:39:13.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:39:13.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:39:13.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-08-28 10:39:13.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-08-28 10:39:13.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.188
2025-08-28 10:39:13.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.346
2025-08-28 10:39:13.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:39:13.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:39:13.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:39:13.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:39:13.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:39:13.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:39:13.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:39:13.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:39:13.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:39:15.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:39:17.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:39:19.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:39:21.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:39:23.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:39:25.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:39:26.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:39:28.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:39:30.847 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:39:30.848 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-28 10:39:30.848 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.35
2025-08-28 10:39:30.848 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:39:30.872 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.98 ms, Average inference time: 7.15 ms

2025-08-28 10:39:30.874 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:39:30.956 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:39:31.098 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch38
2025-08-28 10:39:34.261 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 38/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.981e-03, size: 512, ETA: 3:12:14
2025-08-28 10:39:37.492 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 38/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.981e-03, size: 416, ETA: 3:12:10
2025-08-28 10:39:40.617 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 38/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.981e-03, size: 256, ETA: 3:12:06
2025-08-28 10:39:43.780 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 38/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.2, lr: 1.981e-03, size: 288, ETA: 3:12:02
2025-08-28 10:39:46.952 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 38/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.981e-03, size: 480, ETA: 3:11:58
2025-08-28 10:39:50.160 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 38/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.980e-03, size: 288, ETA: 3:11:54
2025-08-28 10:39:51.587 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:39:57.874 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:40:00.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:40:02.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5443
2025-08-28 10:40:02.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4641
2025-08-28 10:40:02.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2859
2025-08-28 10:40:02.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4314
2025-08-28 10:40:02.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:40:02.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:40:02.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.544
2025-08-28 10:40:02.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.464
2025-08-28 10:40:02.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.286
2025-08-28 10:40:02.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.431
2025-08-28 10:40:02.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:40:02.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:40:02.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:40:02.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:40:02.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:40:02.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:40:02.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:40:02.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:40:02.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:40:05.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:40:07.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:40:09.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:40:12.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:40:14.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:40:16.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:40:18.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:40:20.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:40:23.284 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:40:23.285 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 10:40:23.285 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 10:40:23.285 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:40:23.311 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 1.01 ms, Average inference time: 7.19 ms

2025-08-28 10:40:23.312 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:40:23.392 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:40:23.474 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch39
2025-08-28 10:40:26.500 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 39/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.980e-03, size: 512, ETA: 3:11:46
2025-08-28 10:40:29.785 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 39/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.980e-03, size: 576, ETA: 3:11:44
2025-08-28 10:40:33.304 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 39/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 5.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.980e-03, size: 288, ETA: 3:11:45
2025-08-28 10:40:36.782 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 39/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.980e-03, size: 512, ETA: 3:11:46
2025-08-28 10:40:39.969 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 39/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.979e-03, size: 448, ETA: 3:11:42
2025-08-28 10:40:43.383 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 39/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.979e-03, size: 448, ETA: 3:11:41
2025-08-28 10:40:44.871 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:40:51.167 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:40:54.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:40:56.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5233
2025-08-28 10:40:57.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4380
2025-08-28 10:40:57.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2784
2025-08-28 10:40:57.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4133
2025-08-28 10:40:57.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:40:57.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:40:57.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-08-28 10:40:57.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.438
2025-08-28 10:40:57.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.278
2025-08-28 10:40:57.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.413
2025-08-28 10:40:57.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:40:57.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:40:57.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:40:57.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:40:57.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:40:57.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:40:57.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:40:57.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:40:57.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:40:59.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:41:02.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:41:05.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:41:07.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:41:10.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:41:13.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:41:15.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:41:18.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:41:21.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:41:21.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 10:41:21.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-28 10:41:21.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:41:21.393 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.98 ms, Average inference time: 7.27 ms

2025-08-28 10:41:21.394 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:41:21.476 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:41:21.562 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch40
2025-08-28 10:41:24.641 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 40/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.979e-03, size: 480, ETA: 3:11:35
2025-08-28 10:41:27.951 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 40/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.2, lr: 1.979e-03, size: 288, ETA: 3:11:33
2025-08-28 10:41:31.212 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 40/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.979e-03, size: 544, ETA: 3:11:31
2025-08-28 10:41:34.411 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 40/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.979e-03, size: 448, ETA: 3:11:27
2025-08-28 10:41:37.751 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 40/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.978e-03, size: 448, ETA: 3:11:26
2025-08-28 10:41:41.182 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 40/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.168s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.978e-03, size: 544, ETA: 3:11:25
2025-08-28 10:41:42.689 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:41:48.948 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:41:50.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:41:51.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5388
2025-08-28 10:41:51.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4270
2025-08-28 10:41:51.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2839
2025-08-28 10:41:51.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4166
2025-08-28 10:41:51.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:41:51.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:41:51.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-08-28 10:41:51.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.427
2025-08-28 10:41:51.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.284
2025-08-28 10:41:51.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.417
2025-08-28 10:41:51.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:41:51.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:41:51.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:41:51.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:41:51.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:41:51.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:41:51.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:41:51.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:41:51.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:41:53.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:41:54.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:41:55.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:41:56.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:41:58.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:41:59.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:42:00.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:42:01.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:42:03.260 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:42:03.260 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 10:42:03.260 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 10:42:03.260 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:42:03.268 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.97 ms, Average inference time: 7.17 ms

2025-08-28 10:42:03.270 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:42:03.400 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:42:03.481 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch41
2025-08-28 10:42:06.608 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 41/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.978e-03, size: 512, ETA: 3:11:20
2025-08-28 10:42:09.866 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 41/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.978e-03, size: 448, ETA: 3:11:17
2025-08-28 10:42:13.166 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 41/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.978e-03, size: 544, ETA: 3:11:15
2025-08-28 10:42:16.496 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 41/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.1Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.977e-03, size: 576, ETA: 3:11:13
2025-08-28 10:42:19.826 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 41/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.977e-03, size: 512, ETA: 3:11:12
2025-08-28 10:42:22.926 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 41/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.6, lr: 1.977e-03, size: 256, ETA: 3:11:07
2025-08-28 10:42:24.479 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:42:30.701 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:42:32.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:42:32.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4738
2025-08-28 10:42:33.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4357
2025-08-28 10:42:33.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3034
2025-08-28 10:42:33.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4043
2025-08-28 10:42:33.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:42:33.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:42:33.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-28 10:42:33.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.436
2025-08-28 10:42:33.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-08-28 10:42:33.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.404
2025-08-28 10:42:33.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:42:33.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:42:33.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:42:33.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:42:33.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:42:33.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:42:33.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:42:33.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:42:33.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:42:34.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:42:35.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:42:36.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:42:37.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:42:38.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:42:39.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:42:40.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:42:41.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:42:42.090 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:42:42.090 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-28 10:42:42.090 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 10:42:42.090 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:42:42.098 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 1.01 ms, Average inference time: 7.25 ms

2025-08-28 10:42:42.100 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:42:42.220 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:42:42.319 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch42
2025-08-28 10:42:45.387 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 42/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.977e-03, size: 384, ETA: 3:11:02
2025-08-28 10:42:48.645 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 42/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.977e-03, size: 576, ETA: 3:10:59
2025-08-28 10:42:52.065 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 42/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.977e-03, size: 448, ETA: 3:10:58
2025-08-28 10:42:55.358 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 42/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.976e-03, size: 576, ETA: 3:10:56
2025-08-28 10:42:58.677 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 42/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.976e-03, size: 352, ETA: 3:10:54
2025-08-28 10:43:02.120 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 42/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.976e-03, size: 448, ETA: 3:10:53
2025-08-28 10:43:03.496 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:43:09.699 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:43:12.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:43:13.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5438
2025-08-28 10:43:14.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4321
2025-08-28 10:43:14.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3229
2025-08-28 10:43:14.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4330
2025-08-28 10:43:14.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:43:14.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:43:14.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.544
2025-08-28 10:43:14.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.432
2025-08-28 10:43:14.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.323
2025-08-28 10:43:14.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.433
2025-08-28 10:43:14.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:43:14.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:43:14.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:43:14.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:43:14.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:43:14.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:43:14.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:43:14.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:43:14.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:43:16.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:43:18.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:43:20.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:43:22.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:43:24.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:43:26.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:43:28.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:43:30.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:43:32.901 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:43:32.901 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 10:43:32.901 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 10:43:32.902 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:43:32.930 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.93 ms, Average inference time: 7.02 ms

2025-08-28 10:43:32.931 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:43:33.015 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:43:33.098 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch43
2025-08-28 10:43:36.093 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 43/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.976e-03, size: 288, ETA: 3:10:45
2025-08-28 10:43:39.408 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 43/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.976e-03, size: 576, ETA: 3:10:43
2025-08-28 10:43:42.711 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 43/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.975e-03, size: 384, ETA: 3:10:41
2025-08-28 10:43:46.088 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 43/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.975e-03, size: 288, ETA: 3:10:39
2025-08-28 10:43:49.179 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 43/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.975e-03, size: 352, ETA: 3:10:34
2025-08-28 10:43:52.261 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 43/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.975e-03, size: 416, ETA: 3:10:29
2025-08-28 10:43:53.758 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:43:59.956 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:44:01.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:44:02.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5300
2025-08-28 10:44:02.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4286
2025-08-28 10:44:02.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2734
2025-08-28 10:44:02.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4107
2025-08-28 10:44:02.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:44:02.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:44:02.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 10:44:02.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.429
2025-08-28 10:44:02.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.273
2025-08-28 10:44:02.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.411
2025-08-28 10:44:02.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:44:02.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:44:02.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:44:02.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:44:02.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:44:02.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:44:02.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:44:02.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:44:02.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:44:03.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:44:05.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:44:06.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:44:07.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:44:08.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:44:09.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:44:11.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:44:12.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:44:13.404 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:44:13.404 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 10:44:13.405 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-28 10:44:13.405 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:44:13.412 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.93 ms, Average inference time: 7.14 ms

2025-08-28 10:44:13.413 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:44:13.488 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:44:13.577 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch44
2025-08-28 10:44:16.828 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 44/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.975e-03, size: 256, ETA: 3:10:26
2025-08-28 10:44:20.102 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 44/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.974e-03, size: 448, ETA: 3:10:23
2025-08-28 10:44:23.383 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 44/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.974e-03, size: 416, ETA: 3:10:20
2025-08-28 10:44:26.654 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 44/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.974e-03, size: 512, ETA: 3:10:18
2025-08-28 10:44:29.881 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 44/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.974e-03, size: 352, ETA: 3:10:15
2025-08-28 10:44:33.128 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 44/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.974e-03, size: 480, ETA: 3:10:12
2025-08-28 10:44:34.566 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:44:40.769 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:44:43.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:44:44.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5226
2025-08-28 10:44:45.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4565
2025-08-28 10:44:45.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3453
2025-08-28 10:44:45.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4415
2025-08-28 10:44:45.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:44:45.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:44:45.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-08-28 10:44:45.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-28 10:44:45.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.345
2025-08-28 10:44:45.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.441
2025-08-28 10:44:45.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:44:45.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:44:45.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:44:45.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:44:45.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:44:45.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:44:45.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:44:45.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:44:45.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:44:47.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:44:49.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:44:51.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:44:53.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:44:55.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:44:57.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:44:59.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:45:01.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:45:03.070 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:45:03.070 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 10:45:03.070 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 10:45:03.070 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:45:03.096 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.94 ms, Average inference time: 7.25 ms

2025-08-28 10:45:03.097 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:45:03.181 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:45:03.268 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch45
2025-08-28 10:45:06.326 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 45/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.973e-03, size: 416, ETA: 3:10:05
2025-08-28 10:45:09.521 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 45/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.973e-03, size: 544, ETA: 3:10:01
2025-08-28 10:45:12.884 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 45/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.973e-03, size: 448, ETA: 3:10:00
2025-08-28 10:45:16.095 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 45/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.973e-03, size: 480, ETA: 3:09:56
2025-08-28 10:45:19.324 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 45/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.973e-03, size: 256, ETA: 3:09:53
2025-08-28 10:45:22.552 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 45/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.972e-03, size: 256, ETA: 3:09:50
2025-08-28 10:45:24.089 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:45:30.243 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:45:32.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:45:33.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4867
2025-08-28 10:45:33.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4128
2025-08-28 10:45:33.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2104
2025-08-28 10:45:33.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3700
2025-08-28 10:45:33.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:45:33.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:45:33.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-08-28 10:45:33.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-28 10:45:33.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.210
2025-08-28 10:45:33.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.370
2025-08-28 10:45:33.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:45:33.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:45:33.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:45:33.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:45:33.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:45:33.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:45:33.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:45:33.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:45:33.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:45:35.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:45:36.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:45:38.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:45:39.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:45:41.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:45:42.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:45:44.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:45:45.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:45:47.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:45:47.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-08-28 10:45:47.401 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-28 10:45:47.401 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:45:47.457 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.96 ms, Average inference time: 7.26 ms

2025-08-28 10:45:47.458 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:45:47.588 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:45:47.676 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch46
2025-08-28 10:45:51.025 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 46/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.972e-03, size: 288, ETA: 3:09:48
2025-08-28 10:45:54.197 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 46/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.972e-03, size: 384, ETA: 3:09:44
2025-08-28 10:45:57.398 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 46/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.6, lr: 1.972e-03, size: 576, ETA: 3:09:40
2025-08-28 10:46:00.815 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 46/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.972e-03, size: 544, ETA: 3:09:39
2025-08-28 10:46:04.062 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 46/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.971e-03, size: 320, ETA: 3:09:36
2025-08-28 10:46:07.297 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 46/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.1, lr: 1.971e-03, size: 288, ETA: 3:09:33
2025-08-28 10:46:08.671 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:46:14.826 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:46:17.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:46:20.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5425
2025-08-28 10:46:20.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4671
2025-08-28 10:46:20.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3227
2025-08-28 10:46:20.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4441
2025-08-28 10:46:20.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:46:20.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:46:20.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-08-28 10:46:20.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.467
2025-08-28 10:46:20.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.323
2025-08-28 10:46:20.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.444
2025-08-28 10:46:20.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:46:20.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:46:20.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:46:20.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:46:20.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:46:20.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:46:20.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:46:20.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:46:20.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:46:23.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:46:25.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:46:28.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:46:31.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:46:33.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:46:36.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:46:38.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:46:41.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:46:43.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:46:43.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 10:46:43.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 10:46:43.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:46:44.005 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.96 ms, Average inference time: 7.13 ms

2025-08-28 10:46:44.006 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:46:44.091 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:46:44.172 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch47
2025-08-28 10:46:47.150 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 47/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.971e-03, size: 544, ETA: 3:09:25
2025-08-28 10:46:50.480 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 47/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.971e-03, size: 288, ETA: 3:09:23
2025-08-28 10:46:53.690 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 47/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.971e-03, size: 512, ETA: 3:09:19
2025-08-28 10:46:56.934 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 47/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.970e-03, size: 320, ETA: 3:09:16
2025-08-28 10:47:00.165 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 47/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 7.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.970e-03, size: 352, ETA: 3:09:13
2025-08-28 10:47:03.391 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 47/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.970e-03, size: 256, ETA: 3:09:10
2025-08-28 10:47:04.889 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:47:11.283 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:47:13.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:47:15.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5153
2025-08-28 10:47:15.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4482
2025-08-28 10:47:16.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2674
2025-08-28 10:47:16.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4103
2025-08-28 10:47:16.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:47:16.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:47:16.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 10:47:16.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.448
2025-08-28 10:47:16.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.267
2025-08-28 10:47:16.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.410
2025-08-28 10:47:16.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:47:16.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:47:16.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:47:16.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:47:16.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:47:16.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:47:16.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:47:16.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:47:16.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:47:18.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:47:20.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:47:22.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:47:24.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:47:26.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:47:28.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:47:30.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:47:32.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:47:35.008 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:47:35.008 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 10:47:35.008 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-28 10:47:35.009 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:47:35.034 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.95 ms, Average inference time: 7.15 ms

2025-08-28 10:47:35.036 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:47:35.123 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:47:35.210 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch48
2025-08-28 10:47:38.509 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 48/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 1.0, lr: 1.970e-03, size: 512, ETA: 3:09:06
2025-08-28 10:47:41.809 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 48/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.969e-03, size: 352, ETA: 3:09:04
2025-08-28 10:47:45.068 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 48/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.1, lr: 1.969e-03, size: 576, ETA: 3:09:01
2025-08-28 10:47:48.471 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 48/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.969e-03, size: 512, ETA: 3:09:00
2025-08-28 10:47:51.757 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 48/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.969e-03, size: 576, ETA: 3:08:57
2025-08-28 10:47:55.138 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 48/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.969e-03, size: 288, ETA: 3:08:56
2025-08-28 10:47:56.623 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:48:02.787 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:48:04.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:48:05.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5383
2025-08-28 10:48:05.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4717
2025-08-28 10:48:06.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2687
2025-08-28 10:48:06.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4262
2025-08-28 10:48:06.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:48:06.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:48:06.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.538
2025-08-28 10:48:06.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-08-28 10:48:06.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.269
2025-08-28 10:48:06.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.426
2025-08-28 10:48:06.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:48:06.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:48:06.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:48:06.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:48:06.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:48:06.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:48:06.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:48:06.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:48:06.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:48:07.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:48:08.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:48:10.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:48:11.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:48:13.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:48:14.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:48:16.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:48:17.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:48:18.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:48:18.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 10:48:18.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 10:48:18.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:48:18.963 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.97 ms, Average inference time: 7.27 ms

2025-08-28 10:48:18.964 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:48:19.038 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:48:19.123 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch49
2025-08-28 10:48:22.224 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 49/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.968e-03, size: 512, ETA: 3:08:50
2025-08-28 10:48:25.484 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 49/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.968e-03, size: 384, ETA: 3:08:47
2025-08-28 10:48:28.650 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 49/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.968e-03, size: 320, ETA: 3:08:44
2025-08-28 10:48:31.682 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 49/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.968e-03, size: 320, ETA: 3:08:38
2025-08-28 10:48:34.858 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 49/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.968e-03, size: 512, ETA: 3:08:35
2025-08-28 10:48:38.022 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 49/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.967e-03, size: 384, ETA: 3:08:31
2025-08-28 10:48:39.447 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:48:45.604 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:48:47.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:48:48.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5361
2025-08-28 10:48:48.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4532
2025-08-28 10:48:49.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3313
2025-08-28 10:48:49.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4402
2025-08-28 10:48:49.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:48:49.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:48:49.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-08-28 10:48:49.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.453
2025-08-28 10:48:49.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.331
2025-08-28 10:48:49.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.440
2025-08-28 10:48:49.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:48:49.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:48:49.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:48:49.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:48:49.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:48:49.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:48:49.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:48:49.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:48:49.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:48:50.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:48:52.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:48:53.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:48:55.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:48:56.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:48:58.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:48:59.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:49:01.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:49:02.905 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:49:02.906 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 10:49:02.906 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 10:49:02.906 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:49:02.930 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.03 ms, Average NMS time: 0.95 ms, Average inference time: 6.99 ms

2025-08-28 10:49:02.932 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:49:03.019 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:49:03.106 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch50
2025-08-28 10:49:06.463 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 50/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.967e-03, size: 352, ETA: 3:08:27
2025-08-28 10:49:09.712 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 50/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.967e-03, size: 352, ETA: 3:08:24
2025-08-28 10:49:13.038 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 50/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.164s, data_time: 0.004s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.967e-03, size: 544, ETA: 3:08:22
2025-08-28 10:49:16.729 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 50/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.182s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.966e-03, size: 320, ETA: 3:08:24
2025-08-28 10:49:20.067 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 50/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 1.1, lr: 1.966e-03, size: 256, ETA: 3:08:22
2025-08-28 10:49:23.337 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 50/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.966e-03, size: 256, ETA: 3:08:20
2025-08-28 10:49:24.752 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:49:30.960 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:49:34.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:49:36.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5474
2025-08-28 10:49:36.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4414
2025-08-28 10:49:36.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3184
2025-08-28 10:49:36.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4357
2025-08-28 10:49:36.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:49:36.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:49:36.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.547
2025-08-28 10:49:36.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.441
2025-08-28 10:49:36.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-08-28 10:49:36.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.436
2025-08-28 10:49:36.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:49:36.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:49:36.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:49:36.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:49:36.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:49:36.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:49:36.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:49:36.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:49:36.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:49:39.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:49:42.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:49:44.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:49:47.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:49:49.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:49:52.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:49:55.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:49:57.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:50:00.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:50:00.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 10:50:00.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 10:50:00.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:50:00.404 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 1.00 ms, Average inference time: 7.28 ms

2025-08-28 10:50:00.405 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:50:00.482 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:50:00.564 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch51
2025-08-28 10:50:03.878 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 51/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.966e-03, size: 352, ETA: 3:08:15
2025-08-28 10:50:07.077 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 51/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.966e-03, size: 416, ETA: 3:08:12
2025-08-28 10:50:10.432 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 51/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.965e-03, size: 320, ETA: 3:08:10
2025-08-28 10:50:13.617 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 51/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.965e-03, size: 448, ETA: 3:08:06
2025-08-28 10:50:16.937 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 51/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.965e-03, size: 480, ETA: 3:08:04
2025-08-28 10:50:20.231 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 51/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.965e-03, size: 512, ETA: 3:08:02
2025-08-28 10:50:21.657 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:50:27.961 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:50:29.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:50:30.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5251
2025-08-28 10:50:30.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4717
2025-08-28 10:50:30.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2800
2025-08-28 10:50:30.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4256
2025-08-28 10:50:30.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:50:30.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:50:30.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 10:50:30.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-08-28 10:50:30.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.280
2025-08-28 10:50:30.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.426
2025-08-28 10:50:30.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:50:30.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:50:30.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:50:30.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:50:30.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:50:30.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:50:30.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:50:30.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:50:30.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:50:31.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:50:33.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:50:34.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:50:35.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:50:36.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:50:38.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:50:39.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:50:40.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:50:41.808 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:50:41.809 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-28 10:50:41.809 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 10:50:41.809 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:50:41.816 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.96 ms, Average inference time: 7.22 ms

2025-08-28 10:50:41.818 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:50:41.892 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:50:41.979 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch52
2025-08-28 10:50:45.205 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 52/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.964e-03, size: 544, ETA: 3:07:57
2025-08-28 10:50:48.433 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 52/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.964e-03, size: 384, ETA: 3:07:54
2025-08-28 10:50:51.788 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 52/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.964e-03, size: 320, ETA: 3:07:52
2025-08-28 10:50:55.050 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 52/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.964e-03, size: 416, ETA: 3:07:49
2025-08-28 10:50:58.352 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 52/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.963e-03, size: 384, ETA: 3:07:46
2025-08-28 10:51:01.762 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 52/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.963e-03, size: 320, ETA: 3:07:45
2025-08-28 10:51:03.190 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:51:09.467 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:51:12.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:51:14.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5242
2025-08-28 10:51:14.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4615
2025-08-28 10:51:14.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3329
2025-08-28 10:51:14.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4395
2025-08-28 10:51:14.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:51:14.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:51:14.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-08-28 10:51:14.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.461
2025-08-28 10:51:14.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-08-28 10:51:14.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.440
2025-08-28 10:51:14.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:51:14.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:51:14.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:51:14.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:51:14.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:51:14.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:51:14.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:51:14.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:51:14.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:51:16.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:51:19.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:51:21.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:51:24.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:51:26.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:51:28.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:51:31.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:51:33.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:51:35.678 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:51:35.678 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 10:51:35.679 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 10:51:35.679 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:51:35.704 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.96 ms, Average inference time: 7.16 ms

2025-08-28 10:51:35.705 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:51:35.828 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:51:35.911 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch53
2025-08-28 10:51:38.992 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 53/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.963e-03, size: 320, ETA: 3:07:39
2025-08-28 10:51:42.162 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 53/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.963e-03, size: 384, ETA: 3:07:35
2025-08-28 10:51:45.454 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 53/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.963e-03, size: 576, ETA: 3:07:32
2025-08-28 10:51:48.775 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 53/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.962e-03, size: 576, ETA: 3:07:30
2025-08-28 10:51:52.107 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 53/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.962e-03, size: 320, ETA: 3:07:28
2025-08-28 10:51:55.343 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 53/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.962e-03, size: 384, ETA: 3:07:24
2025-08-28 10:51:56.773 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:52:02.935 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:52:05.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:52:06.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5263
2025-08-28 10:52:07.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4433
2025-08-28 10:52:07.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3120
2025-08-28 10:52:07.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4272
2025-08-28 10:52:07.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:52:07.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:52:07.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-08-28 10:52:07.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.443
2025-08-28 10:52:07.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-08-28 10:52:07.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.427
2025-08-28 10:52:07.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:52:07.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:52:07.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:52:07.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:52:07.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:52:07.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:52:07.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:52:07.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:52:07.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:52:09.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:52:11.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:52:13.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:52:14.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:52:16.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:52:18.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:52:20.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:52:22.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:52:24.496 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:52:24.496 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 10:52:24.497 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 10:52:24.497 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:52:24.521 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 1.01 ms, Average inference time: 7.13 ms

2025-08-28 10:52:24.524 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:52:24.603 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:52:24.689 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch54
2025-08-28 10:52:27.940 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 54/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.962e-03, size: 448, ETA: 3:07:20
2025-08-28 10:52:31.250 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 54/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.961e-03, size: 512, ETA: 3:07:17
2025-08-28 10:52:34.501 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 54/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.961e-03, size: 320, ETA: 3:07:14
2025-08-28 10:52:37.726 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 54/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.961e-03, size: 576, ETA: 3:07:11
2025-08-28 10:52:41.022 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 54/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.961e-03, size: 256, ETA: 3:07:08
2025-08-28 10:52:44.299 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 54/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.960e-03, size: 384, ETA: 3:07:05
2025-08-28 10:52:45.762 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:52:52.102 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:52:55.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:52:57.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5482
2025-08-28 10:52:57.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4430
2025-08-28 10:52:57.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3481
2025-08-28 10:52:57.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4464
2025-08-28 10:52:57.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:52:57.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:52:57.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.548
2025-08-28 10:52:57.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.443
2025-08-28 10:52:57.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.348
2025-08-28 10:52:57.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.446
2025-08-28 10:52:57.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:52:57.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:52:57.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:52:57.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:52:57.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:52:57.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:52:57.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:52:57.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:52:57.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:53:00.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:53:02.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:53:05.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:53:07.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:53:09.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:53:12.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:53:14.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:53:17.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:53:19.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:53:19.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 10:53:19.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 10:53:19.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:53:19.715 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.98 ms, Average inference time: 7.15 ms

2025-08-28 10:53:19.717 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:53:19.799 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:53:19.881 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch55
2025-08-28 10:53:22.914 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 55/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.960e-03, size: 352, ETA: 3:06:59
2025-08-28 10:53:26.100 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 55/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.960e-03, size: 352, ETA: 3:06:55
2025-08-28 10:53:29.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 55/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.960e-03, size: 512, ETA: 3:06:53
2025-08-28 10:53:32.697 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 55/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.2, lr: 1.959e-03, size: 576, ETA: 3:06:50
2025-08-28 10:53:36.068 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 55/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.959e-03, size: 576, ETA: 3:06:49
2025-08-28 10:53:39.350 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 55/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.959e-03, size: 352, ETA: 3:06:46
2025-08-28 10:53:40.826 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:53:47.172 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:53:48.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:53:49.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5391
2025-08-28 10:53:50.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4172
2025-08-28 10:53:50.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2538
2025-08-28 10:53:50.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4034
2025-08-28 10:53:50.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:53:50.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:53:50.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-08-28 10:53:50.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.417
2025-08-28 10:53:50.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.254
2025-08-28 10:53:50.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.403
2025-08-28 10:53:50.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:53:50.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:53:50.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:53:50.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:53:50.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:53:50.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:53:50.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:53:50.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:53:50.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:53:51.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:53:52.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:53:54.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:53:55.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:53:56.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:53:58.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:53:59.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:54:00.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:54:02.128 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:54:02.128 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-28 10:54:02.128 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 10:54:02.128 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:54:02.136 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.95 ms, Average inference time: 7.21 ms

2025-08-28 10:54:02.137 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:54:02.220 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:54:02.309 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch56
2025-08-28 10:54:05.503 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 56/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.959e-03, size: 416, ETA: 3:06:41
2025-08-28 10:54:08.717 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 56/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.958e-03, size: 416, ETA: 3:06:38
2025-08-28 10:54:12.186 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 56/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.958e-03, size: 288, ETA: 3:06:37
2025-08-28 10:54:15.370 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 56/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.6, cls_loss: 0.9, lr: 1.958e-03, size: 416, ETA: 3:06:33
2025-08-28 10:54:18.672 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 56/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.958e-03, size: 384, ETA: 3:06:31
2025-08-28 10:54:21.804 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 56/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.2, lr: 1.957e-03, size: 320, ETA: 3:06:26
2025-08-28 10:54:23.245 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:54:29.690 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:54:31.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:54:33.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5609
2025-08-28 10:54:33.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4306
2025-08-28 10:54:33.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3094
2025-08-28 10:54:33.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4336
2025-08-28 10:54:33.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:54:33.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:54:33.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.561
2025-08-28 10:54:33.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-08-28 10:54:33.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.309
2025-08-28 10:54:33.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-08-28 10:54:33.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:54:33.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:54:33.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:54:33.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:54:33.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:54:33.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:54:33.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:54:33.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:54:33.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:54:35.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:54:37.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:54:39.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:54:41.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:54:43.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:54:45.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:54:46.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:54:48.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:54:50.778 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:54:50.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 10:54:50.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 10:54:50.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:54:50.804 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.97 ms, Average inference time: 7.16 ms

2025-08-28 10:54:50.805 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:54:50.886 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:54:50.968 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch57
2025-08-28 10:54:54.204 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 57/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.957e-03, size: 416, ETA: 3:06:21
2025-08-28 10:54:57.492 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 57/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.957e-03, size: 320, ETA: 3:06:18
2025-08-28 10:55:00.837 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 57/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.957e-03, size: 544, ETA: 3:06:16
2025-08-28 10:55:04.207 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 57/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.956e-03, size: 352, ETA: 3:06:15
2025-08-28 10:55:07.363 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 57/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.956e-03, size: 352, ETA: 3:06:11
2025-08-28 10:55:10.633 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 57/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.956e-03, size: 576, ETA: 3:06:08
2025-08-28 10:55:12.213 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:55:18.411 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:55:20.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:55:21.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5391
2025-08-28 10:55:21.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4569
2025-08-28 10:55:22.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2825
2025-08-28 10:55:22.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4262
2025-08-28 10:55:22.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:55:22.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:55:22.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-08-28 10:55:22.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-28 10:55:22.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.282
2025-08-28 10:55:22.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.426
2025-08-28 10:55:22.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:55:22.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:55:22.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:55:22.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:55:22.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:55:22.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:55:22.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:55:22.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:55:22.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:55:23.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:55:25.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:55:26.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:55:28.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:55:29.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:55:31.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:55:32.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:55:34.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:55:35.829 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:55:35.829 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 10:55:35.829 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 10:55:35.830 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:55:35.854 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.97 ms, Average inference time: 7.14 ms

2025-08-28 10:55:35.855 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:55:35.973 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:55:36.092 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch58
2025-08-28 10:55:39.354 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 58/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.956e-03, size: 352, ETA: 3:06:05
2025-08-28 10:55:42.498 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 58/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.955e-03, size: 320, ETA: 3:06:01
2025-08-28 10:55:45.682 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 58/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.955e-03, size: 384, ETA: 3:05:57
2025-08-28 10:55:49.061 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 58/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.955e-03, size: 352, ETA: 3:05:55
2025-08-28 10:55:52.334 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 58/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.955e-03, size: 416, ETA: 3:05:53
2025-08-28 10:55:55.494 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 58/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.954e-03, size: 416, ETA: 3:05:49
2025-08-28 10:55:56.910 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:56:03.191 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:56:05.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:56:07.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5564
2025-08-28 10:56:07.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4719
2025-08-28 10:56:08.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2940
2025-08-28 10:56:08.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4408
2025-08-28 10:56:08.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:56:08.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:56:08.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.556
2025-08-28 10:56:08.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-08-28 10:56:08.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.294
2025-08-28 10:56:08.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.441
2025-08-28 10:56:08.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:56:08.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:56:08.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:56:08.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:56:08.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:56:08.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:56:08.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:56:08.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:56:08.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:56:10.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:56:12.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:56:14.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:56:16.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:56:19.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:56:21.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:56:23.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:56:25.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:56:28.149 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:56:28.149 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 10:56:28.149 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 10:56:28.149 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:56:28.177 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.96 ms, Average inference time: 7.10 ms

2025-08-28 10:56:28.179 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:56:28.258 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:56:28.339 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch59
2025-08-28 10:56:31.584 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 59/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.954e-03, size: 544, ETA: 3:05:44
2025-08-28 10:56:34.824 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 59/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.1, lr: 1.954e-03, size: 544, ETA: 3:05:41
2025-08-28 10:56:38.086 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 59/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 1.6, cls_loss: 0.6, lr: 1.954e-03, size: 544, ETA: 3:05:38
2025-08-28 10:56:41.316 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 59/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.953e-03, size: 384, ETA: 3:05:35
2025-08-28 10:56:44.494 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 59/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.953e-03, size: 320, ETA: 3:05:31
2025-08-28 10:56:47.641 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 59/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.2, lr: 1.953e-03, size: 416, ETA: 3:05:27
2025-08-28 10:56:49.068 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:56:55.320 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:56:58.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:57:00.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5010
2025-08-28 10:57:00.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4171
2025-08-28 10:57:00.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2886
2025-08-28 10:57:00.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4022
2025-08-28 10:57:00.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:57:00.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:57:00.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-08-28 10:57:00.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.417
2025-08-28 10:57:00.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.289
2025-08-28 10:57:00.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.402
2025-08-28 10:57:00.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:57:00.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:57:00.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:57:00.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:57:00.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:57:00.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:57:00.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:57:00.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:57:00.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:57:02.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:57:05.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:57:07.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:57:10.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:57:12.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:57:14.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:57:17.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:57:19.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:57:21.830 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:57:21.830 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-28 10:57:21.830 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 10:57:21.831 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:57:21.856 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.99 ms, Average inference time: 7.20 ms

2025-08-28 10:57:21.858 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:57:21.940 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:57:22.025 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch60
2025-08-28 10:57:25.039 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 60/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.952e-03, size: 288, ETA: 3:05:20
2025-08-28 10:57:28.394 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 60/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.952e-03, size: 288, ETA: 3:05:18
2025-08-28 10:57:31.674 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 60/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.952e-03, size: 480, ETA: 3:05:15
2025-08-28 10:57:34.982 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 60/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.9, lr: 1.952e-03, size: 544, ETA: 3:05:12
2025-08-28 10:57:38.333 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 60/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 6.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.951e-03, size: 416, ETA: 3:05:10
2025-08-28 10:57:41.530 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 60/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.951e-03, size: 320, ETA: 3:05:07
2025-08-28 10:57:42.955 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:57:49.256 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:57:50.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:57:51.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5448
2025-08-28 10:57:52.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4461
2025-08-28 10:57:52.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3026
2025-08-28 10:57:52.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4312
2025-08-28 10:57:52.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:57:52.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:57:52.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-08-28 10:57:52.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.446
2025-08-28 10:57:52.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-08-28 10:57:52.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.431
2025-08-28 10:57:52.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:57:52.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:57:52.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:57:52.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:57:52.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:57:52.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:57:52.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:57:52.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:57:52.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:57:53.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:57:54.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:57:56.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:57:57.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:57:58.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:57:59.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:58:01.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:58:02.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:58:03.829 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:58:03.829 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 10:58:03.829 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 10:58:03.829 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:58:03.837 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.96 ms, Average inference time: 7.15 ms

2025-08-28 10:58:03.840 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:58:03.917 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:58:03.996 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch61
2025-08-28 10:58:07.095 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 61/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.951e-03, size: 544, ETA: 3:05:01
2025-08-28 10:58:10.364 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 61/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.951e-03, size: 320, ETA: 3:04:58
2025-08-28 10:58:13.622 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 61/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.950e-03, size: 416, ETA: 3:04:55
2025-08-28 10:58:16.849 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 61/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.950e-03, size: 512, ETA: 3:04:51
2025-08-28 10:58:20.203 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 61/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.165s, data_time: 0.005s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.950e-03, size: 384, ETA: 3:04:49
2025-08-28 10:58:23.643 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 61/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.950e-03, size: 544, ETA: 3:04:48
2025-08-28 10:58:25.122 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:58:31.351 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:58:33.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:58:34.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5037
2025-08-28 10:58:34.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4357
2025-08-28 10:58:34.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2545
2025-08-28 10:58:34.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3980
2025-08-28 10:58:34.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:58:34.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:58:34.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-08-28 10:58:34.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.436
2025-08-28 10:58:34.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.254
2025-08-28 10:58:34.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.398
2025-08-28 10:58:34.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:58:34.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:58:34.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:58:34.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:58:34.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:58:34.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:58:34.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:58:34.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:58:34.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:58:36.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:58:37.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:58:39.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:58:40.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:58:41.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:58:43.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:58:44.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:58:46.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:58:47.794 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:58:47.794 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-28 10:58:47.794 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 10:58:47.794 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:58:47.808 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.96 ms, Average inference time: 7.11 ms

2025-08-28 10:58:47.809 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:58:47.917 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:58:48.035 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch62
2025-08-28 10:58:51.137 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 62/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.2, lr: 1.949e-03, size: 512, ETA: 3:04:42
2025-08-28 10:58:54.393 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 62/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.949e-03, size: 576, ETA: 3:04:39
2025-08-28 10:58:57.628 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 62/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.949e-03, size: 512, ETA: 3:04:36
2025-08-28 10:59:00.855 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 62/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.948e-03, size: 416, ETA: 3:04:33
2025-08-28 10:59:04.147 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 62/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.948e-03, size: 448, ETA: 3:04:30
2025-08-28 10:59:07.400 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 62/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.948e-03, size: 544, ETA: 3:04:27
2025-08-28 10:59:08.886 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:59:15.117 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 10:59:17.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 10:59:19.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5623
2025-08-28 10:59:19.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4615
2025-08-28 10:59:19.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3141
2025-08-28 10:59:19.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4460
2025-08-28 10:59:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 10:59:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 10:59:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.562
2025-08-28 10:59:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-08-28 10:59:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-08-28 10:59:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.446
2025-08-28 10:59:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 10:59:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 10:59:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 10:59:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 10:59:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 10:59:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 10:59:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 10:59:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 10:59:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 10:59:21.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 10:59:23.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 10:59:25.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 10:59:27.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 10:59:29.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 10:59:31.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 10:59:33.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 10:59:35.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 10:59:37.068 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 10:59:37.068 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 10:59:37.068 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 10:59:37.069 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 10:59:37.094 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.96 ms, Average inference time: 7.16 ms

2025-08-28 10:59:37.096 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:59:37.176 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 10:59:37.260 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch63
2025-08-28 10:59:40.357 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 63/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.948e-03, size: 512, ETA: 3:04:22
2025-08-28 10:59:43.526 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 63/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 0.9, lr: 1.947e-03, size: 544, ETA: 3:04:18
2025-08-28 10:59:46.864 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 63/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.947e-03, size: 512, ETA: 3:04:16
2025-08-28 10:59:50.158 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 63/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.947e-03, size: 320, ETA: 3:04:13
2025-08-28 10:59:53.638 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 63/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.171s, data_time: 0.003s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.946e-03, size: 480, ETA: 3:04:12
2025-08-28 10:59:56.951 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 63/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.946e-03, size: 576, ETA: 3:04:10
2025-08-28 10:59:58.428 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:00:04.628 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:00:07.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:00:08.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5139
2025-08-28 11:00:09.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4102
2025-08-28 11:00:09.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2851
2025-08-28 11:00:09.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4031
2025-08-28 11:00:09.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:00:09.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:00:09.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.514
2025-08-28 11:00:09.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-08-28 11:00:09.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.285
2025-08-28 11:00:09.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.403
2025-08-28 11:00:09.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:00:09.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:00:09.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:00:09.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:00:09.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:00:09.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:00:09.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:00:09.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:00:09.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:00:11.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:00:13.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:00:15.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:00:17.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:00:19.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:00:21.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:00:23.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:00:25.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:00:27.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:00:27.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:00:27.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 11:00:27.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:00:27.197 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.94 ms, Average inference time: 7.13 ms

2025-08-28 11:00:27.198 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:00:27.284 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:00:27.369 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch64
2025-08-28 11:00:30.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 64/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.946e-03, size: 384, ETA: 3:04:04
2025-08-28 11:00:33.597 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 64/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.946e-03, size: 352, ETA: 3:04:00
2025-08-28 11:00:36.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 64/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.945e-03, size: 544, ETA: 3:03:58
2025-08-28 11:00:40.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 64/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.945e-03, size: 352, ETA: 3:03:55
2025-08-28 11:00:43.487 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 64/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.945e-03, size: 416, ETA: 3:03:52
2025-08-28 11:00:46.724 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 64/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.944e-03, size: 416, ETA: 3:03:49
2025-08-28 11:00:48.213 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:00:54.508 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:00:58.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:01:00.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5572
2025-08-28 11:01:01.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4652
2025-08-28 11:01:01.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3237
2025-08-28 11:01:01.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4487
2025-08-28 11:01:01.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:01:01.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:01:01.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.557
2025-08-28 11:01:01.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.465
2025-08-28 11:01:01.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.324
2025-08-28 11:01:01.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.449
2025-08-28 11:01:01.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:01:01.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:01:01.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:01:01.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:01:01.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:01:01.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:01:01.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:01:01.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:01:01.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:01:04.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:01:07.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:01:10.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:01:13.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:01:16.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:01:19.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:01:22.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:01:25.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:01:28.702 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:01:28.703 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 11:01:28.703 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 11:01:28.703 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:01:28.723 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.99 ms, Average inference time: 7.30 ms

2025-08-28 11:01:28.725 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:01:28.848 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:01:28.931 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch65
2025-08-28 11:01:31.977 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 65/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.2Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.944e-03, size: 480, ETA: 3:03:43
2025-08-28 11:01:35.174 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 65/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.944e-03, size: 544, ETA: 3:03:39
2025-08-28 11:01:38.407 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 65/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.944e-03, size: 256, ETA: 3:03:36
2025-08-28 11:01:41.522 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 65/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.943e-03, size: 320, ETA: 3:03:32
2025-08-28 11:01:44.692 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 65/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.943e-03, size: 480, ETA: 3:03:28
2025-08-28 11:01:47.838 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 65/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.943e-03, size: 416, ETA: 3:03:24
2025-08-28 11:01:49.283 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:01:55.556 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:01:57.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:01:59.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5506
2025-08-28 11:01:59.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4359
2025-08-28 11:01:59.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2739
2025-08-28 11:01:59.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4201
2025-08-28 11:01:59.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:01:59.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:01:59.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-08-28 11:01:59.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.436
2025-08-28 11:01:59.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.274
2025-08-28 11:01:59.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.420
2025-08-28 11:01:59.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:01:59.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:01:59.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:01:59.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:01:59.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:01:59.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:01:59.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:01:59.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:01:59.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:02:01.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:02:03.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:02:04.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:02:06.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:02:08.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:02:10.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:02:12.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:02:13.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:02:15.701 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:02:15.702 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 11:02:15.702 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 11:02:15.702 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:02:15.726 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.96 ms, Average inference time: 7.24 ms

2025-08-28 11:02:15.728 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:02:15.807 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:02:15.893 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch66
2025-08-28 11:02:19.009 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 66/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.942e-03, size: 512, ETA: 3:03:19
2025-08-28 11:02:22.176 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 66/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.942e-03, size: 352, ETA: 3:03:15
2025-08-28 11:02:25.284 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 66/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.942e-03, size: 384, ETA: 3:03:11
2025-08-28 11:02:28.568 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 66/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.9, lr: 1.942e-03, size: 448, ETA: 3:03:08
2025-08-28 11:02:31.838 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 66/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.6, lr: 1.941e-03, size: 256, ETA: 3:03:05
2025-08-28 11:02:35.070 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 66/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.941e-03, size: 256, ETA: 3:03:02
2025-08-28 11:02:36.468 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:02:42.683 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:02:45.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:02:46.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5373
2025-08-28 11:02:47.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4394
2025-08-28 11:02:47.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3308
2025-08-28 11:02:47.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4358
2025-08-28 11:02:47.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:02:47.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:02:47.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-08-28 11:02:47.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-08-28 11:02:47.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.331
2025-08-28 11:02:47.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.436
2025-08-28 11:02:47.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:02:47.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:02:47.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:02:47.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:02:47.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:02:47.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:02:47.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:02:47.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:02:47.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:02:49.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:02:51.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:02:53.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:02:55.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:02:57.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:02:59.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:03:01.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:03:03.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:03:05.311 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:03:05.311 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:03:05.312 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:03:05.312 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:03:05.337 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.95 ms, Average inference time: 7.15 ms

2025-08-28 11:03:05.339 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:03:05.417 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:03:05.500 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch67
2025-08-28 11:03:08.598 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 67/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.941e-03, size: 256, ETA: 3:02:56
2025-08-28 11:03:11.875 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 67/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.940e-03, size: 256, ETA: 3:02:52
2025-08-28 11:03:15.045 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 67/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.940e-03, size: 416, ETA: 3:02:49
2025-08-28 11:03:18.201 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 67/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.940e-03, size: 320, ETA: 3:02:45
2025-08-28 11:03:21.469 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 67/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.161s, data_time: 0.005s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.939e-03, size: 480, ETA: 3:02:42
2025-08-28 11:03:24.709 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 67/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.939e-03, size: 480, ETA: 3:02:39
2025-08-28 11:03:26.105 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:03:32.225 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:03:33.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:03:35.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5303
2025-08-28 11:03:35.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4452
2025-08-28 11:03:35.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3557
2025-08-28 11:03:35.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4437
2025-08-28 11:03:35.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:03:35.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:03:35.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 11:03:35.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.445
2025-08-28 11:03:35.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-08-28 11:03:35.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.444
2025-08-28 11:03:35.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:03:35.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:03:35.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:03:35.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:03:35.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:03:35.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:03:35.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:03:35.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:03:35.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:03:36.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:03:38.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:03:39.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:03:40.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:03:42.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:03:43.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:03:45.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:03:46.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:03:47.785 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:03:47.786 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 11:03:47.786 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:03:47.786 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:03:47.796 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.94 ms, Average inference time: 7.04 ms

2025-08-28 11:03:47.797 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:03:47.871 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:03:48.013 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch68
2025-08-28 11:03:51.110 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 68/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.939e-03, size: 544, ETA: 3:02:33
2025-08-28 11:03:54.323 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 68/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.939e-03, size: 448, ETA: 3:02:29
2025-08-28 11:03:57.566 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 68/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.938e-03, size: 320, ETA: 3:02:26
2025-08-28 11:04:00.742 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 68/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.938e-03, size: 416, ETA: 3:02:23
2025-08-28 11:04:04.031 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 68/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.938e-03, size: 480, ETA: 3:02:20
2025-08-28 11:04:07.216 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 68/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.937e-03, size: 288, ETA: 3:02:16
2025-08-28 11:04:08.727 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:04:14.944 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:04:17.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:04:18.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5454
2025-08-28 11:04:18.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4665
2025-08-28 11:04:18.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3108
2025-08-28 11:04:18.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4409
2025-08-28 11:04:18.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:04:18.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:04:18.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-08-28 11:04:18.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.467
2025-08-28 11:04:18.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.311
2025-08-28 11:04:18.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.441
2025-08-28 11:04:18.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:04:18.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:04:18.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:04:18.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:04:18.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:04:18.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:04:18.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:04:18.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:04:18.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:04:20.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:04:22.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:04:24.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:04:26.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:04:28.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:04:30.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:04:31.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:04:33.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:04:35.432 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:04:35.432 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 11:04:35.433 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:04:35.433 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:04:35.457 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.96 ms, Average inference time: 7.22 ms

2025-08-28 11:04:35.458 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:04:35.536 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:04:35.621 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch69
2025-08-28 11:04:38.760 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 69/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.937e-03, size: 384, ETA: 3:02:11
2025-08-28 11:04:41.872 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 69/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 8.8, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 4.2, cls_loss: 1.0, lr: 1.937e-03, size: 352, ETA: 3:02:07
2025-08-28 11:04:45.189 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 69/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.936e-03, size: 256, ETA: 3:02:05
2025-08-28 11:04:48.365 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 69/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.936e-03, size: 288, ETA: 3:02:01
2025-08-28 11:04:51.596 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 69/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 7.7, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.936e-03, size: 416, ETA: 3:01:58
2025-08-28 11:04:54.802 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 69/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.936e-03, size: 512, ETA: 3:01:54
2025-08-28 11:04:56.284 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:05:02.452 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:05:04.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:05:05.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5212
2025-08-28 11:05:05.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4291
2025-08-28 11:05:05.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2327
2025-08-28 11:05:05.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3943
2025-08-28 11:05:05.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:05:05.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:05:05.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-08-28 11:05:05.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.429
2025-08-28 11:05:05.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.233
2025-08-28 11:05:05.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.394
2025-08-28 11:05:05.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:05:05.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:05:05.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:05:05.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:05:05.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:05:05.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:05:05.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:05:05.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:05:05.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:05:07.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:05:08.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:05:10.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:05:11.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:05:13.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:05:14.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:05:16.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:05:17.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:05:19.130 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:05:19.130 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 11:05:19.131 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-28 11:05:19.131 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:05:19.155 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.94 ms, Average inference time: 7.11 ms

2025-08-28 11:05:19.156 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:05:19.240 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:05:19.327 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch70
2025-08-28 11:05:22.449 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 70/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.935e-03, size: 384, ETA: 3:01:49
2025-08-28 11:05:25.596 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 70/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.935e-03, size: 256, ETA: 3:01:45
2025-08-28 11:05:28.849 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 70/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.935e-03, size: 448, ETA: 3:01:42
2025-08-28 11:05:32.075 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 70/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.934e-03, size: 544, ETA: 3:01:39
2025-08-28 11:05:35.307 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 70/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.934e-03, size: 384, ETA: 3:01:36
2025-08-28 11:05:38.558 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 70/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.934e-03, size: 352, ETA: 3:01:33
2025-08-28 11:05:39.990 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:05:46.397 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:05:50.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:05:52.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5559
2025-08-28 11:05:53.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4374
2025-08-28 11:05:53.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3008
2025-08-28 11:05:53.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4314
2025-08-28 11:05:53.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:05:53.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:05:53.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.556
2025-08-28 11:05:53.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-28 11:05:53.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.301
2025-08-28 11:05:53.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.431
2025-08-28 11:05:53.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:05:53.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:05:53.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:05:53.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:05:53.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:05:53.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:05:53.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:05:53.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:05:53.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:05:56.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:05:59.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:06:02.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:06:05.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:06:08.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:06:11.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:06:14.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:06:17.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:06:20.676 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:06:20.677 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:06:20.677 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 11:06:20.677 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:06:20.703 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.99 ms, Average inference time: 7.17 ms

2025-08-28 11:06:20.704 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:06:20.784 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:06:20.867 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch71
2025-08-28 11:06:23.905 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 71/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.2, lr: 1.933e-03, size: 480, ETA: 3:01:27
2025-08-28 11:06:27.116 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 71/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.933e-03, size: 352, ETA: 3:01:23
2025-08-28 11:06:30.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 71/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.933e-03, size: 288, ETA: 3:01:19
2025-08-28 11:06:33.525 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 71/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.932e-03, size: 384, ETA: 3:01:16
2025-08-28 11:06:36.970 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 71/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.171s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.932e-03, size: 544, ETA: 3:01:15
2025-08-28 11:06:40.276 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 71/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.932e-03, size: 320, ETA: 3:01:12
2025-08-28 11:06:41.673 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:06:48.014 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:06:51.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:06:54.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5253
2025-08-28 11:06:54.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4449
2025-08-28 11:06:54.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2968
2025-08-28 11:06:54.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4223
2025-08-28 11:06:54.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:06:54.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:06:54.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 11:06:54.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.445
2025-08-28 11:06:54.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.297
2025-08-28 11:06:54.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.422
2025-08-28 11:06:54.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:06:54.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:06:54.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:06:54.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:06:54.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:06:54.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:06:54.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:06:54.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:06:54.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:06:58.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:07:01.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:07:04.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:07:07.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:07:10.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:07:13.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:07:16.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:07:19.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:07:22.879 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:07:22.879 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 11:07:22.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 11:07:22.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:07:22.907 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 1.01 ms, Average inference time: 7.16 ms

2025-08-28 11:07:22.908 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:07:22.987 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:07:23.073 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch72
2025-08-28 11:07:26.294 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 72/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.931e-03, size: 480, ETA: 3:01:07
2025-08-28 11:07:29.478 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 72/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.931e-03, size: 352, ETA: 3:01:04
2025-08-28 11:07:32.701 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 72/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.931e-03, size: 352, ETA: 3:01:00
2025-08-28 11:07:35.963 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 72/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.931e-03, size: 544, ETA: 3:00:57
2025-08-28 11:07:39.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 72/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.930e-03, size: 384, ETA: 3:00:55
2025-08-28 11:07:42.763 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 72/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.930e-03, size: 384, ETA: 3:00:54
2025-08-28 11:07:44.197 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:07:50.277 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:07:53.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:07:54.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5613
2025-08-28 11:07:55.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4525
2025-08-28 11:07:55.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3401
2025-08-28 11:07:55.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4513
2025-08-28 11:07:55.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:07:55.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:07:55.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.561
2025-08-28 11:07:55.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-28 11:07:55.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-08-28 11:07:55.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.451
2025-08-28 11:07:55.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:07:55.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:07:55.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:07:55.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:07:55.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:07:55.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:07:55.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:07:55.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:07:55.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:07:57.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:07:59.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:08:02.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:08:04.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:08:06.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:08:09.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:08:11.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:08:13.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:08:15.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:08:15.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:08:15.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 11:08:15.821 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:08:15.849 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.99 ms, Average inference time: 7.18 ms

2025-08-28 11:08:15.850 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:08:15.928 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:08:16.011 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch73
2025-08-28 11:08:19.136 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 73/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.929e-03, size: 512, ETA: 3:00:48
2025-08-28 11:08:22.289 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 73/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.929e-03, size: 416, ETA: 3:00:44
2025-08-28 11:08:25.647 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 73/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.929e-03, size: 288, ETA: 3:00:42
2025-08-28 11:08:28.855 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 73/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.929e-03, size: 288, ETA: 3:00:38
2025-08-28 11:08:32.084 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 73/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.928e-03, size: 480, ETA: 3:00:35
2025-08-28 11:08:35.433 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 73/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.928e-03, size: 384, ETA: 3:00:33
2025-08-28 11:08:36.906 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:08:43.248 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:08:44.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:08:45.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4975
2025-08-28 11:08:46.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4136
2025-08-28 11:08:46.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2945
2025-08-28 11:08:46.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4019
2025-08-28 11:08:46.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:08:46.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:08:46.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-08-28 11:08:46.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-08-28 11:08:46.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-08-28 11:08:46.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.402
2025-08-28 11:08:46.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:08:46.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:08:46.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:08:46.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:08:46.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:08:46.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:08:46.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:08:46.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:08:46.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:08:47.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:08:48.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:08:49.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:08:51.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:08:52.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:08:53.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:08:54.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:08:55.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:08:57.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:08:57.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-28 11:08:57.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 11:08:57.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:08:57.205 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.93 ms, Average inference time: 7.10 ms

2025-08-28 11:08:57.206 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:08:57.286 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:08:57.372 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch74
2025-08-28 11:09:00.571 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 74/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.928e-03, size: 576, ETA: 3:00:28
2025-08-28 11:09:03.891 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 74/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.927e-03, size: 320, ETA: 3:00:26
2025-08-28 11:09:07.112 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 74/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.927e-03, size: 448, ETA: 3:00:22
2025-08-28 11:09:10.268 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 74/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.927e-03, size: 512, ETA: 3:00:19
2025-08-28 11:09:13.833 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 74/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.176s, data_time: 0.003s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.926e-03, size: 576, ETA: 3:00:18
2025-08-28 11:09:17.131 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 74/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.926e-03, size: 256, ETA: 3:00:15
2025-08-28 11:09:18.705 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:09:25.312 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:09:27.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:09:29.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5578
2025-08-28 11:09:29.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4491
2025-08-28 11:09:29.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2960
2025-08-28 11:09:29.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4343
2025-08-28 11:09:29.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:09:29.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:09:29.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.558
2025-08-28 11:09:29.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-08-28 11:09:29.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.296
2025-08-28 11:09:29.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-08-28 11:09:29.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:09:29.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:09:29.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:09:29.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:09:29.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:09:29.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:09:29.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:09:29.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:09:29.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:09:31.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:09:33.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:09:35.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:09:37.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:09:39.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:09:41.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:09:43.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:09:45.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:09:47.331 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:09:47.331 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:09:47.331 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 11:09:47.331 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:09:47.357 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.96 ms, Average inference time: 7.22 ms

2025-08-28 11:09:47.358 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:09:47.486 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:09:47.565 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch75
2025-08-28 11:09:50.666 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 75/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.926e-03, size: 480, ETA: 3:00:10
2025-08-28 11:09:53.971 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 75/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.925e-03, size: 448, ETA: 3:00:08
2025-08-28 11:09:57.122 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 75/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.925e-03, size: 448, ETA: 3:00:04
2025-08-28 11:10:00.310 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 75/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.925e-03, size: 544, ETA: 3:00:00
2025-08-28 11:10:03.524 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 75/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.2, lr: 1.924e-03, size: 352, ETA: 2:59:57
2025-08-28 11:10:06.733 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 75/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.924e-03, size: 448, ETA: 2:59:54
2025-08-28 11:10:08.133 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:10:14.378 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:10:17.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:10:20.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5303
2025-08-28 11:10:20.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4339
2025-08-28 11:10:20.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3125
2025-08-28 11:10:20.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4256
2025-08-28 11:10:20.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:10:20.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:10:20.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 11:10:20.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.434
2025-08-28 11:10:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.313
2025-08-28 11:10:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.426
2025-08-28 11:10:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:10:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:10:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:10:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:10:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:10:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:10:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:10:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:10:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:10:23.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:10:26.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:10:29.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:10:32.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:10:35.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:10:37.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:10:40.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:10:43.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:10:46.695 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:10:46.696 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:10:46.696 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 11:10:46.696 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:10:46.721 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.08 ms, Average NMS time: 0.99 ms, Average inference time: 7.06 ms

2025-08-28 11:10:46.722 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:10:46.810 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:10:46.897 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch76
2025-08-28 11:10:50.096 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 76/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.924e-03, size: 384, ETA: 2:59:48
2025-08-28 11:10:53.356 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 76/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.3, lr: 1.923e-03, size: 384, ETA: 2:59:45
2025-08-28 11:10:56.662 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 76/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.923e-03, size: 288, ETA: 2:59:42
2025-08-28 11:10:59.969 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 76/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.162s, data_time: 0.005s, total_loss: 7.5, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.923e-03, size: 320, ETA: 2:59:40
2025-08-28 11:11:03.160 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 76/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.922e-03, size: 320, ETA: 2:59:36
2025-08-28 11:11:06.385 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 76/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.922e-03, size: 544, ETA: 2:59:33
2025-08-28 11:11:07.848 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:11:14.158 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:11:16.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:11:17.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5314
2025-08-28 11:11:17.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4754
2025-08-28 11:11:17.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3225
2025-08-28 11:11:17.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4431
2025-08-28 11:11:17.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:11:17.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:11:17.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-08-28 11:11:17.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-08-28 11:11:17.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.322
2025-08-28 11:11:17.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.443
2025-08-28 11:11:17.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:11:17.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:11:17.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:11:17.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:11:17.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:11:17.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:11:17.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:11:17.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:11:17.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:11:19.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:11:20.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:11:22.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:11:23.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:11:25.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:11:26.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:11:28.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:11:29.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:11:31.478 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:11:31.478 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:11:31.478 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:11:31.478 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:11:31.501 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.97 ms, Average inference time: 7.18 ms

2025-08-28 11:11:31.504 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:11:31.585 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:11:31.670 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch77
2025-08-28 11:11:34.741 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 77/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.922e-03, size: 416, ETA: 2:59:27
2025-08-28 11:11:38.048 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 77/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.921e-03, size: 576, ETA: 2:59:25
2025-08-28 11:11:41.365 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 77/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.921e-03, size: 576, ETA: 2:59:22
2025-08-28 11:11:44.717 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 77/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 6.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.921e-03, size: 320, ETA: 2:59:20
2025-08-28 11:11:47.932 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 77/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.920e-03, size: 256, ETA: 2:59:16
2025-08-28 11:11:51.206 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 77/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.920e-03, size: 288, ETA: 2:59:13
2025-08-28 11:11:52.602 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:11:58.882 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:12:01.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:12:02.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4822
2025-08-28 11:12:03.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3370
2025-08-28 11:12:03.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2566
2025-08-28 11:12:03.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3586
2025-08-28 11:12:03.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:12:03.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:12:03.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-28 11:12:03.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.337
2025-08-28 11:12:03.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.257
2025-08-28 11:12:03.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.359
2025-08-28 11:12:03.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:12:03.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:12:03.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:12:03.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:12:03.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:12:03.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:12:03.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:12:03.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:12:03.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:12:05.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:12:07.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:12:09.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:12:11.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:12:13.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:12:15.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:12:17.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:12:19.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:12:21.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:12:21.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-28 11:12:21.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.36
2025-08-28 11:12:21.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:12:21.261 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 1.00 ms, Average inference time: 7.15 ms

2025-08-28 11:12:21.262 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:12:21.342 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:12:21.429 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch78
2025-08-28 11:12:24.503 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 78/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.920e-03, size: 320, ETA: 2:59:07
2025-08-28 11:12:27.663 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 78/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.919e-03, size: 512, ETA: 2:59:04
2025-08-28 11:12:30.923 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 78/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.919e-03, size: 512, ETA: 2:59:01
2025-08-28 11:12:34.291 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 78/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.166s, data_time: 0.006s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.1, lr: 1.919e-03, size: 384, ETA: 2:58:58
2025-08-28 11:12:37.531 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 78/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.918e-03, size: 416, ETA: 2:58:55
2025-08-28 11:12:40.795 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 78/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.918e-03, size: 320, ETA: 2:58:52
2025-08-28 11:12:42.225 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:12:48.387 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:12:50.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:12:52.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5445
2025-08-28 11:12:52.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4617
2025-08-28 11:12:52.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3201
2025-08-28 11:12:52.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4421
2025-08-28 11:12:52.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:12:52.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:12:52.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-08-28 11:12:52.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-08-28 11:12:52.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.320
2025-08-28 11:12:52.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.442
2025-08-28 11:12:52.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:12:52.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:12:52.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:12:52.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:12:52.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:12:52.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:12:52.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:12:52.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:12:52.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:12:54.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:12:56.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:12:58.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:13:00.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:13:01.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:13:03.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:13:05.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:13:07.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:13:09.498 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:13:09.498 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:13:09.498 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:13:09.498 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:13:09.523 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.07 ms, Average NMS time: 0.94 ms, Average inference time: 7.01 ms

2025-08-28 11:13:09.525 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:13:09.599 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:13:09.680 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch79
2025-08-28 11:13:12.798 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 79/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.917e-03, size: 416, ETA: 2:58:47
2025-08-28 11:13:16.023 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 79/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.917e-03, size: 352, ETA: 2:58:44
2025-08-28 11:13:19.271 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 79/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.917e-03, size: 352, ETA: 2:58:41
2025-08-28 11:13:22.504 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 79/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.916e-03, size: 288, ETA: 2:58:37
2025-08-28 11:13:25.731 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 79/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.916e-03, size: 480, ETA: 2:58:34
2025-08-28 11:13:29.050 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 79/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.916e-03, size: 320, ETA: 2:58:31
2025-08-28 11:13:30.518 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:13:36.784 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:13:41.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:13:43.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5108
2025-08-28 11:13:44.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4179
2025-08-28 11:13:44.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2313
2025-08-28 11:13:44.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3867
2025-08-28 11:13:44.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:13:44.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:13:44.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-08-28 11:13:44.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.418
2025-08-28 11:13:44.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.231
2025-08-28 11:13:44.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.387
2025-08-28 11:13:44.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:13:44.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:13:44.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:13:44.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:13:44.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:13:44.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:13:44.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:13:44.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:13:44.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:13:48.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:13:51.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:13:54.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:13:58.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:14:01.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:14:05.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:14:08.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:14:11.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:14:15.227 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:14:15.227 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-28 11:14:15.227 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-28 11:14:15.228 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:14:15.252 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.99 ms, Average inference time: 7.17 ms

2025-08-28 11:14:15.253 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:14:15.388 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:14:15.519 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch80
2025-08-28 11:14:18.627 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 80/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.915e-03, size: 320, ETA: 2:58:26
2025-08-28 11:14:21.784 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 80/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.915e-03, size: 480, ETA: 2:58:22
2025-08-28 11:14:24.962 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 80/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.915e-03, size: 416, ETA: 2:58:19
2025-08-28 11:14:28.226 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 80/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.3, lr: 1.914e-03, size: 256, ETA: 2:58:16
2025-08-28 11:14:31.438 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 80/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.914e-03, size: 384, ETA: 2:58:12
2025-08-28 11:14:34.593 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 80/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.914e-03, size: 352, ETA: 2:58:09
2025-08-28 11:14:36.025 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:14:42.192 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:14:45.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:14:48.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5435
2025-08-28 11:14:48.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4542
2025-08-28 11:14:48.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3567
2025-08-28 11:14:48.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4515
2025-08-28 11:14:48.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:14:48.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:14:48.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.544
2025-08-28 11:14:48.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.454
2025-08-28 11:14:48.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.357
2025-08-28 11:14:48.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.451
2025-08-28 11:14:48.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:14:48.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:14:48.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:14:48.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:14:48.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:14:48.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:14:48.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:14:48.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:14:48.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:14:52.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:14:55.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:14:58.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:15:01.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:15:04.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:15:07.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:15:10.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:15:13.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:15:17.144 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:15:17.144 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 11:15:17.144 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 11:15:17.144 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:15:17.176 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.96 ms, Average inference time: 7.14 ms

2025-08-28 11:15:17.177 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:15:17.286 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:15:17.399 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch81
2025-08-28 11:15:20.363 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 81/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.913e-03, size: 288, ETA: 2:58:02
2025-08-28 11:15:23.611 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 81/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.913e-03, size: 320, ETA: 2:57:59
2025-08-28 11:15:26.840 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 81/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.3, lr: 1.913e-03, size: 256, ETA: 2:57:56
2025-08-28 11:15:30.016 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 81/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.912e-03, size: 416, ETA: 2:57:52
2025-08-28 11:15:33.182 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 81/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.912e-03, size: 288, ETA: 2:57:49
2025-08-28 11:15:36.551 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 81/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.912e-03, size: 416, ETA: 2:57:46
2025-08-28 11:15:38.004 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:15:44.203 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:15:46.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:15:47.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5491
2025-08-28 11:15:47.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4522
2025-08-28 11:15:47.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3334
2025-08-28 11:15:47.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4449
2025-08-28 11:15:47.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:15:47.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:15:47.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.549
2025-08-28 11:15:47.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-28 11:15:47.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-08-28 11:15:47.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.445
2025-08-28 11:15:47.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:15:47.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:15:47.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:15:47.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:15:47.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:15:47.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:15:47.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:15:47.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:15:47.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:15:49.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:15:51.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:15:52.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:15:54.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:15:56.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:15:57.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:15:59.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:16:01.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:16:02.904 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:16:02.904 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:16:02.904 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:16:02.904 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:16:02.929 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.97 ms, Average inference time: 7.15 ms

2025-08-28 11:16:02.931 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:16:03.013 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:16:03.099 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch82
2025-08-28 11:16:06.229 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 82/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.1, lr: 1.911e-03, size: 512, ETA: 2:57:41
2025-08-28 11:16:09.556 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 82/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.1, lr: 1.911e-03, size: 544, ETA: 2:57:39
2025-08-28 11:16:12.803 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 82/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.910e-03, size: 448, ETA: 2:57:36
2025-08-28 11:16:16.000 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 82/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.910e-03, size: 288, ETA: 2:57:32
2025-08-28 11:16:19.238 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 82/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.910e-03, size: 448, ETA: 2:57:29
2025-08-28 11:16:22.468 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 82/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.909e-03, size: 544, ETA: 2:57:26
2025-08-28 11:16:24.048 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:16:30.445 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:16:32.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:16:34.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5413
2025-08-28 11:16:34.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4699
2025-08-28 11:16:35.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2904
2025-08-28 11:16:35.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4338
2025-08-28 11:16:35.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:16:35.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:16:35.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-08-28 11:16:35.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.470
2025-08-28 11:16:35.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-08-28 11:16:35.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-08-28 11:16:35.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:16:35.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:16:35.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:16:35.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:16:35.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:16:35.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:16:35.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:16:35.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:16:35.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:16:37.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:16:39.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:16:41.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:16:43.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:16:45.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:16:47.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:16:49.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:16:51.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:16:53.610 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:16:53.611 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:16:53.611 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 11:16:53.611 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:16:53.637 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.98 ms, Average inference time: 7.11 ms

2025-08-28 11:16:53.638 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:16:53.710 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:16:53.792 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch83
2025-08-28 11:16:57.020 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 83/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.909e-03, size: 256, ETA: 2:57:22
2025-08-28 11:17:00.241 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 83/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.909e-03, size: 288, ETA: 2:57:19
2025-08-28 11:17:03.504 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 83/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.908e-03, size: 576, ETA: 2:57:16
2025-08-28 11:17:06.882 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 83/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.908e-03, size: 352, ETA: 2:57:13
2025-08-28 11:17:10.116 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 83/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.908e-03, size: 288, ETA: 2:57:10
2025-08-28 11:17:13.477 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 83/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.4, cls_loss: 0.9, lr: 1.907e-03, size: 576, ETA: 2:57:08
2025-08-28 11:17:14.937 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:17:21.179 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:17:23.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:17:24.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5251
2025-08-28 11:17:24.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4769
2025-08-28 11:17:24.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2894
2025-08-28 11:17:24.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4305
2025-08-28 11:17:24.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:17:24.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:17:24.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 11:17:24.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.477
2025-08-28 11:17:24.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.289
2025-08-28 11:17:24.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.431
2025-08-28 11:17:24.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:17:24.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:17:24.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:17:24.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:17:24.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:17:24.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:17:24.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:17:24.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:17:24.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:17:26.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:17:27.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:17:29.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:17:30.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:17:32.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:17:33.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:17:34.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:17:36.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:17:37.855 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:17:37.855 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:17:37.856 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 11:17:37.856 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:17:37.865 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.10 ms, Average NMS time: 0.93 ms, Average inference time: 7.03 ms

2025-08-28 11:17:37.867 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:17:37.993 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:17:38.081 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch84
2025-08-28 11:17:41.255 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 84/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 1.0, lr: 1.907e-03, size: 384, ETA: 2:57:03
2025-08-28 11:17:44.499 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 84/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.906e-03, size: 320, ETA: 2:57:00
2025-08-28 11:17:47.657 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 84/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.906e-03, size: 544, ETA: 2:56:56
2025-08-28 11:17:51.020 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 84/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.906e-03, size: 576, ETA: 2:56:54
2025-08-28 11:17:54.243 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 84/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.905e-03, size: 480, ETA: 2:56:50
2025-08-28 11:17:57.535 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 84/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.905e-03, size: 448, ETA: 2:56:48
2025-08-28 11:17:59.100 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:18:05.293 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:18:08.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:18:10.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5403
2025-08-28 11:18:11.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4287
2025-08-28 11:18:11.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3143
2025-08-28 11:18:11.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4278
2025-08-28 11:18:11.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:18:11.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:18:11.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.540
2025-08-28 11:18:11.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.429
2025-08-28 11:18:11.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-08-28 11:18:11.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.428
2025-08-28 11:18:11.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:18:11.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:18:11.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:18:11.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:18:11.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:18:11.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:18:11.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:18:11.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:18:11.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:18:14.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:18:17.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:18:20.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:18:22.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:18:25.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:18:28.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:18:31.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:18:34.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:18:37.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:18:37.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 11:18:37.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 11:18:37.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:18:37.250 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.05 ms, Average NMS time: 0.96 ms, Average inference time: 7.01 ms

2025-08-28 11:18:37.251 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:18:37.379 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:18:37.461 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch85
2025-08-28 11:18:40.752 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 85/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.904e-03, size: 288, ETA: 2:56:44
2025-08-28 11:18:43.967 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 85/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.904e-03, size: 480, ETA: 2:56:41
2025-08-28 11:18:47.280 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 85/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.904e-03, size: 576, ETA: 2:56:38
2025-08-28 11:18:50.556 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 85/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.903e-03, size: 320, ETA: 2:56:35
2025-08-28 11:18:53.737 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 85/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.903e-03, size: 384, ETA: 2:56:32
2025-08-28 11:18:57.135 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 85/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.168s, data_time: 0.004s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.903e-03, size: 576, ETA: 2:56:30
2025-08-28 11:18:58.822 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:19:05.053 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:19:06.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:19:07.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5373
2025-08-28 11:19:07.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4734
2025-08-28 11:19:07.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3081
2025-08-28 11:19:07.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4396
2025-08-28 11:19:07.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:19:07.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:19:07.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-08-28 11:19:07.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.473
2025-08-28 11:19:07.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.308
2025-08-28 11:19:07.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.440
2025-08-28 11:19:07.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:19:07.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:19:07.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:19:07.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:19:07.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:19:07.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:19:07.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:19:07.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:19:07.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:19:08.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:19:10.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:19:11.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:19:12.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:19:13.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:19:14.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:19:15.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:19:17.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:19:18.191 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:19:18.191 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 11:19:18.192 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:19:18.192 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:19:18.199 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.94 ms, Average inference time: 7.16 ms

2025-08-28 11:19:18.202 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:19:18.283 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:19:18.380 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch86
2025-08-28 11:19:21.762 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 86/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.902e-03, size: 416, ETA: 2:56:27
2025-08-28 11:19:25.157 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 86/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.902e-03, size: 480, ETA: 2:56:25
2025-08-28 11:19:28.447 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 86/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.902e-03, size: 448, ETA: 2:56:22
2025-08-28 11:19:31.620 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 86/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.901e-03, size: 480, ETA: 2:56:19
2025-08-28 11:19:34.862 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 86/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.901e-03, size: 448, ETA: 2:56:16
2025-08-28 11:19:37.990 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 86/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.2, lr: 1.900e-03, size: 288, ETA: 2:56:12
2025-08-28 11:19:39.480 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:19:45.798 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:19:47.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:19:49.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5417
2025-08-28 11:19:49.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4314
2025-08-28 11:19:49.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2898
2025-08-28 11:19:49.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4210
2025-08-28 11:19:49.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:19:49.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:19:49.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-08-28 11:19:49.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.431
2025-08-28 11:19:49.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-08-28 11:19:49.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.421
2025-08-28 11:19:49.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:19:49.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:19:49.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:19:49.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:19:49.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:19:49.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:19:49.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:19:49.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:19:49.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:19:51.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:19:52.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:19:54.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:19:55.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:19:57.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:19:59.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:20:00.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:20:02.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:20:04.081 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:20:04.081 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 11:20:04.082 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 11:20:04.082 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:20:04.109 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.97 ms, Average inference time: 7.27 ms

2025-08-28 11:20:04.111 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:20:04.189 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:20:04.271 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch87
2025-08-28 11:20:07.442 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 87/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.900e-03, size: 288, ETA: 2:56:07
2025-08-28 11:20:10.715 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 87/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.900e-03, size: 352, ETA: 2:56:04
2025-08-28 11:20:13.965 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 87/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.899e-03, size: 320, ETA: 2:56:01
2025-08-28 11:20:17.132 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 87/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 7.4, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.899e-03, size: 448, ETA: 2:55:58
2025-08-28 11:20:20.490 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 87/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.899e-03, size: 256, ETA: 2:55:55
2025-08-28 11:20:23.671 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 87/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.4, lr: 1.898e-03, size: 448, ETA: 2:55:52
2025-08-28 11:20:25.173 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:20:31.307 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:20:34.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:20:35.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5211
2025-08-28 11:20:36.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4659
2025-08-28 11:20:36.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3149
2025-08-28 11:20:36.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4340
2025-08-28 11:20:36.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:20:36.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:20:36.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-08-28 11:20:36.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-08-28 11:20:36.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.315
2025-08-28 11:20:36.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-08-28 11:20:36.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:20:36.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:20:36.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:20:36.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:20:36.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:20:36.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:20:36.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:20:36.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:20:36.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:20:38.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:20:40.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:20:43.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:20:45.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:20:47.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:20:49.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:20:52.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:20:54.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:20:56.650 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:20:56.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 11:20:56.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 11:20:56.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:20:56.679 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.98 ms, Average inference time: 7.11 ms

2025-08-28 11:20:56.680 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:20:56.761 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:20:56.848 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch88
2025-08-28 11:20:59.934 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 88/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.898e-03, size: 320, ETA: 2:55:47
2025-08-28 11:21:03.100 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 88/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.897e-03, size: 512, ETA: 2:55:43
2025-08-28 11:21:06.255 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 88/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.897e-03, size: 512, ETA: 2:55:39
2025-08-28 11:21:09.645 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 88/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.167s, data_time: 0.006s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.897e-03, size: 480, ETA: 2:55:37
2025-08-28 11:21:12.842 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 88/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.896e-03, size: 384, ETA: 2:55:34
2025-08-28 11:21:16.020 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 88/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.896e-03, size: 320, ETA: 2:55:30
2025-08-28 11:21:17.475 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:21:23.632 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:21:25.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:21:27.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5593
2025-08-28 11:21:27.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4615
2025-08-28 11:21:27.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3093
2025-08-28 11:21:27.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4434
2025-08-28 11:21:27.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:21:27.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:21:27.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-08-28 11:21:27.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-08-28 11:21:27.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.309
2025-08-28 11:21:27.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.443
2025-08-28 11:21:27.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:21:27.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:21:27.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:21:27.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:21:27.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:21:27.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:21:27.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:21:27.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:21:27.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:21:29.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:21:31.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:21:33.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:21:35.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:21:37.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:21:39.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:21:41.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:21:43.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:21:45.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:21:45.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:21:45.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:21:45.037 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:21:45.062 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.97 ms, Average inference time: 7.23 ms

2025-08-28 11:21:45.063 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:21:45.195 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:21:45.276 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch89
2025-08-28 11:21:48.332 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 89/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.895e-03, size: 416, ETA: 2:55:24
2025-08-28 11:21:51.617 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 89/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.895e-03, size: 352, ETA: 2:55:21
2025-08-28 11:21:54.948 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 89/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.895e-03, size: 448, ETA: 2:55:19
2025-08-28 11:21:58.218 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 89/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.894e-03, size: 320, ETA: 2:55:16
2025-08-28 11:22:01.602 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 89/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.894e-03, size: 320, ETA: 2:55:13
2025-08-28 11:22:05.060 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 89/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.894e-03, size: 288, ETA: 2:55:11
2025-08-28 11:22:06.467 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:22:12.740 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:22:15.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:22:16.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4824
2025-08-28 11:22:17.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4196
2025-08-28 11:22:17.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2442
2025-08-28 11:22:17.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3820
2025-08-28 11:22:17.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:22:17.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:22:17.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-28 11:22:17.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.420
2025-08-28 11:22:17.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.244
2025-08-28 11:22:17.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.382
2025-08-28 11:22:17.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:22:17.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:22:17.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:22:17.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:22:17.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:22:17.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:22:17.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:22:17.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:22:17.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:22:19.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:22:21.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:22:23.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:22:25.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:22:28.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:22:30.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:22:32.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:22:34.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:22:36.380 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:22:36.380 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 11:22:36.380 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-08-28 11:22:36.380 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:22:36.405 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.97 ms, Average inference time: 7.12 ms

2025-08-28 11:22:36.406 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:22:36.488 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:22:36.578 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch90
2025-08-28 11:22:39.633 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 90/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.893e-03, size: 416, ETA: 2:55:06
2025-08-28 11:22:42.872 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 90/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.893e-03, size: 384, ETA: 2:55:02
2025-08-28 11:22:46.028 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 90/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.892e-03, size: 288, ETA: 2:54:59
2025-08-28 11:22:49.316 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 90/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.892e-03, size: 448, ETA: 2:54:56
2025-08-28 11:22:52.655 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 90/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.892e-03, size: 480, ETA: 2:54:53
2025-08-28 11:22:55.936 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 90/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.891e-03, size: 384, ETA: 2:54:50
2025-08-28 11:22:57.419 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:23:03.777 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:23:06.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:23:07.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5673
2025-08-28 11:23:07.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4598
2025-08-28 11:23:08.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3400
2025-08-28 11:23:08.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4557
2025-08-28 11:23:08.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:23:08.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:23:08.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.567
2025-08-28 11:23:08.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-08-28 11:23:08.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-08-28 11:23:08.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.456
2025-08-28 11:23:08.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:23:08.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:23:08.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:23:08.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:23:08.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:23:08.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:23:08.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:23:08.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:23:08.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:23:09.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:23:11.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:23:13.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:23:15.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:23:17.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:23:19.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:23:21.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:23:23.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:23:25.231 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:23:25.231 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:23:25.231 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 11:23:25.231 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:23:25.256 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.39 ms, Average NMS time: 0.97 ms, Average inference time: 7.37 ms

2025-08-28 11:23:25.257 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:23:25.338 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:23:25.421 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch91
2025-08-28 11:23:28.546 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 91/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.891e-03, size: 512, ETA: 2:54:45
2025-08-28 11:23:31.814 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 91/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.890e-03, size: 544, ETA: 2:54:42
2025-08-28 11:23:35.093 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 91/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.890e-03, size: 256, ETA: 2:54:39
2025-08-28 11:23:38.385 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 91/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.890e-03, size: 416, ETA: 2:54:36
2025-08-28 11:23:41.660 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 91/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.889e-03, size: 352, ETA: 2:54:33
2025-08-28 11:23:44.823 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 91/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.4, lr: 1.889e-03, size: 352, ETA: 2:54:30
2025-08-28 11:23:46.222 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:23:52.435 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:23:54.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:23:56.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5173
2025-08-28 11:23:56.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4447
2025-08-28 11:23:57.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3071
2025-08-28 11:23:57.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4230
2025-08-28 11:23:57.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:23:57.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:23:57.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-08-28 11:23:57.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.445
2025-08-28 11:23:57.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.307
2025-08-28 11:23:57.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.423
2025-08-28 11:23:57.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:23:57.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:23:57.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:23:57.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:23:57.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:23:57.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:23:57.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:23:57.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:23:57.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:23:59.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:24:01.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:24:03.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:24:05.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:24:07.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:24:09.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:24:11.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:24:13.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:24:15.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:24:15.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:24:15.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 11:24:15.528 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:24:15.552 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.96 ms, Average inference time: 7.18 ms

2025-08-28 11:24:15.554 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:24:15.637 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:24:15.726 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch92
2025-08-28 11:24:18.822 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 92/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.888e-03, size: 544, ETA: 2:54:24
2025-08-28 11:24:22.089 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 92/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.888e-03, size: 288, ETA: 2:54:21
2025-08-28 11:24:25.458 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 92/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.887e-03, size: 448, ETA: 2:54:19
2025-08-28 11:24:28.662 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 92/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.887e-03, size: 352, ETA: 2:54:15
2025-08-28 11:24:31.837 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 92/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.887e-03, size: 288, ETA: 2:54:12
2025-08-28 11:24:35.003 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 92/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.886e-03, size: 512, ETA: 2:54:08
2025-08-28 11:24:36.461 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:24:42.793 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:24:45.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:24:46.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5322
2025-08-28 11:24:47.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4290
2025-08-28 11:24:47.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3150
2025-08-28 11:24:47.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4254
2025-08-28 11:24:47.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:24:47.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:24:47.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-08-28 11:24:47.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.429
2025-08-28 11:24:47.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.315
2025-08-28 11:24:47.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.425
2025-08-28 11:24:47.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:24:47.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:24:47.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:24:47.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:24:47.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:24:47.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:24:47.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:24:47.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:24:47.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:24:49.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:24:51.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:24:53.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:24:54.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:24:56.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:24:58.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:25:00.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:25:02.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:25:04.718 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:25:04.719 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 11:25:04.719 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 11:25:04.719 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:25:04.745 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.40 ms, Average NMS time: 0.96 ms, Average inference time: 7.36 ms

2025-08-28 11:25:04.746 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:25:04.875 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:25:04.958 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch93
2025-08-28 11:25:07.987 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 93/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.886e-03, size: 352, ETA: 2:54:02
2025-08-28 11:25:11.440 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 93/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.885e-03, size: 384, ETA: 2:54:00
2025-08-28 11:25:14.675 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 93/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.885e-03, size: 288, ETA: 2:53:57
2025-08-28 11:25:17.802 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 93/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.885e-03, size: 288, ETA: 2:53:53
2025-08-28 11:25:21.037 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 93/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.884e-03, size: 288, ETA: 2:53:50
2025-08-28 11:25:24.229 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 93/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.884e-03, size: 480, ETA: 2:53:47
2025-08-28 11:25:25.733 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:25:31.928 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:25:33.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:25:35.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4982
2025-08-28 11:25:35.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4227
2025-08-28 11:25:35.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2524
2025-08-28 11:25:35.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3911
2025-08-28 11:25:35.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:25:35.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:25:35.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-28 11:25:35.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.423
2025-08-28 11:25:35.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.252
2025-08-28 11:25:35.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.391
2025-08-28 11:25:35.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:25:35.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:25:35.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:25:35.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:25:35.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:25:35.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:25:35.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:25:35.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:25:35.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:25:36.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:25:38.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:25:40.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:25:41.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:25:43.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:25:44.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:25:46.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:25:47.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:25:49.472 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:25:49.473 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 11:25:49.474 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-28 11:25:49.474 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:25:49.505 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.93 ms, Average inference time: 7.18 ms

2025-08-28 11:25:49.506 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:25:49.665 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:25:49.768 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch94
2025-08-28 11:25:52.941 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 94/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.2, lr: 1.883e-03, size: 576, ETA: 2:53:42
2025-08-28 11:25:56.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 94/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.883e-03, size: 320, ETA: 2:53:41
2025-08-28 11:25:59.679 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 94/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.883e-03, size: 416, ETA: 2:53:37
2025-08-28 11:26:02.913 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 94/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 8.1, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.8, lr: 1.882e-03, size: 384, ETA: 2:53:34
2025-08-28 11:26:06.169 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 94/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.882e-03, size: 576, ETA: 2:53:31
2025-08-28 11:26:09.425 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 94/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.881e-03, size: 512, ETA: 2:53:28
2025-08-28 11:26:10.931 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:26:17.202 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:26:19.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:26:21.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5470
2025-08-28 11:26:22.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4707
2025-08-28 11:26:22.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3136
2025-08-28 11:26:22.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4438
2025-08-28 11:26:22.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:26:22.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:26:22.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.547
2025-08-28 11:26:22.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-08-28 11:26:22.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-08-28 11:26:22.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.444
2025-08-28 11:26:22.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:26:22.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:26:22.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:26:22.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:26:22.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:26:22.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:26:22.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:26:22.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:26:22.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:26:24.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:26:26.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:26:29.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:26:31.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:26:33.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:26:35.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:26:38.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:26:40.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:26:42.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:26:42.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:26:42.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:26:42.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:26:42.987 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.99 ms, Average inference time: 7.17 ms

2025-08-28 11:26:42.988 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:26:43.071 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:26:43.154 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch95
2025-08-28 11:26:46.239 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 95/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.881e-03, size: 256, ETA: 2:53:23
2025-08-28 11:26:49.619 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 95/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.881e-03, size: 384, ETA: 2:53:21
2025-08-28 11:26:52.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 95/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.880e-03, size: 544, ETA: 2:53:18
2025-08-28 11:26:56.286 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 95/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 6.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.880e-03, size: 256, ETA: 2:53:15
2025-08-28 11:26:59.609 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 95/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.2, lr: 1.879e-03, size: 320, ETA: 2:53:13
2025-08-28 11:27:02.763 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 95/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.879e-03, size: 512, ETA: 2:53:09
2025-08-28 11:27:04.252 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:27:10.461 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:27:13.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:27:15.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5119
2025-08-28 11:27:15.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4197
2025-08-28 11:27:15.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3040
2025-08-28 11:27:15.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4119
2025-08-28 11:27:15.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:27:15.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:27:15.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-08-28 11:27:15.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.420
2025-08-28 11:27:15.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-08-28 11:27:15.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.412
2025-08-28 11:27:15.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:27:15.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:27:15.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:27:15.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:27:15.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:27:15.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:27:15.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:27:15.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:27:15.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:27:18.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:27:20.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:27:22.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:27:25.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:27:27.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:27:29.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:27:32.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:27:34.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:27:36.774 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:27:36.774 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 11:27:36.774 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-28 11:27:36.775 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:27:36.799 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.10 ms, Average NMS time: 0.96 ms, Average inference time: 7.06 ms

2025-08-28 11:27:36.801 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:27:36.877 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:27:36.966 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch96
2025-08-28 11:27:40.114 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 96/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.878e-03, size: 544, ETA: 2:53:04
2025-08-28 11:27:43.489 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 96/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.878e-03, size: 320, ETA: 2:53:02
2025-08-28 11:27:46.721 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 96/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.2, cls_loss: 0.9, lr: 1.878e-03, size: 320, ETA: 2:52:58
2025-08-28 11:27:49.983 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 96/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.877e-03, size: 256, ETA: 2:52:55
2025-08-28 11:27:53.072 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 96/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.877e-03, size: 288, ETA: 2:52:51
2025-08-28 11:27:56.300 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 96/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.876e-03, size: 416, ETA: 2:52:48
2025-08-28 11:27:57.680 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:28:03.982 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:28:06.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:28:08.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5417
2025-08-28 11:28:09.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4652
2025-08-28 11:28:09.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3280
2025-08-28 11:28:09.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4450
2025-08-28 11:28:09.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:28:09.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:28:09.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-08-28 11:28:09.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.465
2025-08-28 11:28:09.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.328
2025-08-28 11:28:09.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.445
2025-08-28 11:28:09.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:28:09.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:28:09.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:28:09.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:28:09.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:28:09.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:28:09.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:28:09.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:28:09.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:28:11.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:28:13.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:28:16.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:28:18.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:28:20.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:28:23.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:28:25.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:28:27.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:28:30.048 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:28:30.048 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:28:30.048 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:28:30.048 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:28:30.073 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.96 ms, Average inference time: 7.20 ms

2025-08-28 11:28:30.075 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:28:30.153 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:28:30.234 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch97
2025-08-28 11:28:33.457 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 97/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.876e-03, size: 480, ETA: 2:52:43
2025-08-28 11:28:36.677 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 97/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.876e-03, size: 416, ETA: 2:52:40
2025-08-28 11:28:39.931 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 97/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.875e-03, size: 416, ETA: 2:52:37
2025-08-28 11:28:43.215 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 97/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.875e-03, size: 576, ETA: 2:52:34
2025-08-28 11:28:46.569 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 97/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.874e-03, size: 576, ETA: 2:52:31
2025-08-28 11:28:49.837 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 97/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.874e-03, size: 544, ETA: 2:52:28
2025-08-28 11:28:51.322 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:28:57.373 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:28:59.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:29:01.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5286
2025-08-28 11:29:01.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4787
2025-08-28 11:29:01.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3427
2025-08-28 11:29:01.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4500
2025-08-28 11:29:01.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:29:01.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:29:01.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-08-28 11:29:01.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-08-28 11:29:01.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.343
2025-08-28 11:29:01.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.450
2025-08-28 11:29:01.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:29:01.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:29:01.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:29:01.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:29:01.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:29:01.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:29:01.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:29:01.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:29:01.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:29:03.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:29:05.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:29:07.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:29:09.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:29:11.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:29:13.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:29:15.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:29:17.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:29:19.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:29:19.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:29:19.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 11:29:19.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:29:19.061 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.95 ms, Average inference time: 7.08 ms

2025-08-28 11:29:19.062 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:29:19.143 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:29:19.229 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch98
2025-08-28 11:29:22.341 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 98/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.873e-03, size: 256, ETA: 2:52:23
2025-08-28 11:29:25.493 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 98/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.873e-03, size: 448, ETA: 2:52:19
2025-08-28 11:29:28.901 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 98/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.873e-03, size: 544, ETA: 2:52:17
2025-08-28 11:29:32.111 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 98/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 7.6, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.872e-03, size: 352, ETA: 2:52:14
2025-08-28 11:29:35.477 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 98/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.872e-03, size: 416, ETA: 2:52:11
2025-08-28 11:29:38.733 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 98/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.871e-03, size: 288, ETA: 2:52:08
2025-08-28 11:29:40.121 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:29:46.487 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:29:51.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:29:55.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5414
2025-08-28 11:29:56.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4718
2025-08-28 11:29:56.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2915
2025-08-28 11:29:56.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4349
2025-08-28 11:29:56.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:29:56.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:29:56.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-08-28 11:29:56.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-08-28 11:29:56.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.292
2025-08-28 11:29:56.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.435
2025-08-28 11:29:56.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:29:56.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:29:56.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:29:56.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:29:56.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:29:56.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:29:56.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:29:56.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:29:56.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:30:01.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:30:05.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:30:10.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:30:14.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:30:19.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:30:23.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:30:28.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:30:33.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:30:37.525 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:30:37.526 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:30:37.526 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 11:30:37.526 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:30:37.553 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.98 ms, Average inference time: 7.12 ms

2025-08-28 11:30:37.554 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:30:37.701 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:30:37.786 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch99
2025-08-28 11:30:40.976 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 99/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.871e-03, size: 512, ETA: 2:52:03
2025-08-28 11:30:44.229 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 99/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.870e-03, size: 352, ETA: 2:52:00
2025-08-28 11:30:47.453 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 99/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.870e-03, size: 512, ETA: 2:51:57
2025-08-28 11:30:50.810 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 99/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.870e-03, size: 480, ETA: 2:51:54
2025-08-28 11:30:54.014 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 99/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.869e-03, size: 576, ETA: 2:51:51
2025-08-28 11:30:57.308 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 99/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.869e-03, size: 352, ETA: 2:51:48
2025-08-28 11:30:58.736 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:31:04.916 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:31:06.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:31:07.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5211
2025-08-28 11:31:07.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4654
2025-08-28 11:31:08.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3162
2025-08-28 11:31:08.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4342
2025-08-28 11:31:08.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:31:08.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:31:08.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-08-28 11:31:08.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.465
2025-08-28 11:31:08.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-28 11:31:08.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-08-28 11:31:08.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:31:08.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:31:08.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:31:08.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:31:08.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:31:08.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:31:08.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:31:08.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:31:08.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:31:09.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:31:10.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:31:11.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:31:13.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:31:14.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:31:15.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:31:17.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:31:18.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:31:19.838 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:31:19.838 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 11:31:19.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 11:31:19.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:31:19.847 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.99 ms, Average inference time: 7.12 ms

2025-08-28 11:31:19.848 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:31:19.923 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:31:20.011 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch100
2025-08-28 11:31:23.211 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 100/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.868e-03, size: 448, ETA: 2:51:43
2025-08-28 11:31:26.657 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 100/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.868e-03, size: 416, ETA: 2:51:41
2025-08-28 11:31:29.999 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 100/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.867e-03, size: 576, ETA: 2:51:38
2025-08-28 11:31:33.320 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 100/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.867e-03, size: 512, ETA: 2:51:35
2025-08-28 11:31:36.656 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 100/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.867e-03, size: 352, ETA: 2:51:33
2025-08-28 11:31:39.820 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 100/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.866e-03, size: 512, ETA: 2:51:29
2025-08-28 11:31:41.260 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:31:47.591 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:31:49.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:31:51.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5323
2025-08-28 11:31:51.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4655
2025-08-28 11:31:51.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2812
2025-08-28 11:31:51.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4263
2025-08-28 11:31:51.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:31:51.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:31:51.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-08-28 11:31:51.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-08-28 11:31:51.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.281
2025-08-28 11:31:51.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.426
2025-08-28 11:31:51.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:31:51.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:31:51.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:31:51.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:31:51.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:31:51.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:31:51.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:31:51.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:31:51.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:31:53.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:31:55.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:31:57.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:31:59.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:32:01.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:32:03.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:32:05.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:32:06.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:32:08.747 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:32:08.747 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:32:08.747 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 11:32:08.747 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:32:08.772 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 1.00 ms, Average inference time: 7.09 ms

2025-08-28 11:32:08.774 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:32:08.853 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:32:08.935 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch101
2025-08-28 11:32:12.138 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 101/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.2, lr: 1.866e-03, size: 480, ETA: 2:51:24
2025-08-28 11:32:15.363 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 101/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.2, lr: 1.865e-03, size: 352, ETA: 2:51:21
2025-08-28 11:32:18.643 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 101/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.865e-03, size: 416, ETA: 2:51:18
2025-08-28 11:32:21.761 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 101/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.864e-03, size: 416, ETA: 2:51:15
2025-08-28 11:32:24.940 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 101/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.864e-03, size: 288, ETA: 2:51:11
2025-08-28 11:32:28.050 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 101/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.864e-03, size: 384, ETA: 2:51:07
2025-08-28 11:32:29.474 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:32:35.734 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:32:38.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:32:40.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5117
2025-08-28 11:32:40.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4805
2025-08-28 11:32:41.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3244
2025-08-28 11:32:41.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4389
2025-08-28 11:32:41.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:32:41.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:32:41.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-08-28 11:32:41.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.480
2025-08-28 11:32:41.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.324
2025-08-28 11:32:41.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.439
2025-08-28 11:32:41.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:32:41.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:32:41.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:32:41.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:32:41.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:32:41.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:32:41.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:32:41.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:32:41.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:32:43.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:32:45.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:32:48.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:32:50.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:32:53.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:32:55.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:32:57.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:33:00.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:33:02.646 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:33:02.647 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 11:33:02.647 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:33:02.647 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:33:02.672 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.95 ms, Average inference time: 7.19 ms

2025-08-28 11:33:02.673 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:33:02.807 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:33:02.896 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch102
2025-08-28 11:33:05.917 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 102/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.863e-03, size: 384, ETA: 2:51:02
2025-08-28 11:33:09.318 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 102/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.863e-03, size: 384, ETA: 2:50:59
2025-08-28 11:33:12.621 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 102/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.862e-03, size: 256, ETA: 2:50:56
2025-08-28 11:33:15.915 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 102/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.862e-03, size: 384, ETA: 2:50:54
2025-08-28 11:33:19.086 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 102/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.0, conf_loss: 1.8, cls_loss: 0.7, lr: 1.861e-03, size: 352, ETA: 2:50:50
2025-08-28 11:33:22.328 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 102/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.861e-03, size: 320, ETA: 2:50:47
2025-08-28 11:33:23.771 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:33:30.019 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:33:31.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:33:33.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5311
2025-08-28 11:33:33.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4879
2025-08-28 11:33:33.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3179
2025-08-28 11:33:33.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4456
2025-08-28 11:33:33.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:33:33.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:33:33.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-08-28 11:33:33.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-08-28 11:33:33.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-08-28 11:33:33.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.446
2025-08-28 11:33:33.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:33:33.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:33:33.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:33:33.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:33:33.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:33:33.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:33:33.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:33:33.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:33:33.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:33:34.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:33:36.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:33:38.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:33:39.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:33:41.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:33:42.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:33:44.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:33:45.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:33:47.387 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:33:47.388 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:33:47.388 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 11:33:47.388 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:33:47.407 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.99 ms, Average inference time: 7.18 ms

2025-08-28 11:33:47.408 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:33:47.537 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:33:47.620 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch103
2025-08-28 11:33:50.787 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 103/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.860e-03, size: 256, ETA: 2:50:42
2025-08-28 11:33:54.035 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 103/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.860e-03, size: 544, ETA: 2:50:39
2025-08-28 11:33:57.296 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 103/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.859e-03, size: 480, ETA: 2:50:36
2025-08-28 11:34:00.615 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 103/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.859e-03, size: 480, ETA: 2:50:33
2025-08-28 11:34:03.817 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 103/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.9, lr: 1.859e-03, size: 288, ETA: 2:50:29
2025-08-28 11:34:07.074 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 103/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.858e-03, size: 448, ETA: 2:50:26
2025-08-28 11:34:08.571 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:34:14.785 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:34:16.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:34:17.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5197
2025-08-28 11:34:17.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4429
2025-08-28 11:34:17.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2373
2025-08-28 11:34:17.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4000
2025-08-28 11:34:17.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:34:17.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:34:17.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-08-28 11:34:17.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.443
2025-08-28 11:34:17.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.237
2025-08-28 11:34:17.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.400
2025-08-28 11:34:17.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:34:17.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:34:17.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:34:17.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:34:17.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:34:17.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:34:17.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:34:17.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:34:17.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:34:19.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:34:20.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:34:21.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:34:22.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:34:24.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:34:25.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:34:26.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:34:28.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:34:29.338 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:34:29.338 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-28 11:34:29.339 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 11:34:29.339 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:34:29.346 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.98 ms, Average inference time: 7.16 ms

2025-08-28 11:34:29.348 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:34:29.429 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:34:29.570 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch104
2025-08-28 11:34:32.650 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 104/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 1.0, lr: 1.858e-03, size: 320, ETA: 2:50:21
2025-08-28 11:34:35.853 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 104/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.857e-03, size: 352, ETA: 2:50:18
2025-08-28 11:34:39.048 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 104/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.2, lr: 1.857e-03, size: 512, ETA: 2:50:14
2025-08-28 11:34:42.496 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 104/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.1, lr: 1.856e-03, size: 384, ETA: 2:50:12
2025-08-28 11:34:45.757 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 104/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.856e-03, size: 320, ETA: 2:50:09
2025-08-28 11:34:48.982 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 104/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.856e-03, size: 416, ETA: 2:50:06
2025-08-28 11:34:50.388 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:34:56.643 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:34:58.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:34:59.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5392
2025-08-28 11:34:59.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4627
2025-08-28 11:34:59.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3491
2025-08-28 11:34:59.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4503
2025-08-28 11:34:59.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:34:59.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:34:59.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-08-28 11:34:59.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.463
2025-08-28 11:34:59.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.349
2025-08-28 11:34:59.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.450
2025-08-28 11:34:59.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:34:59.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:34:59.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:34:59.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:34:59.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:34:59.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:34:59.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:34:59.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:34:59.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:35:00.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:35:01.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:35:03.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:35:04.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:35:05.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:35:06.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:35:07.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:35:08.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:35:10.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:35:10.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:35:10.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 11:35:10.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:35:10.208 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.97 ms, Average inference time: 7.17 ms

2025-08-28 11:35:10.209 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:35:10.288 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:35:10.370 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch105
2025-08-28 11:35:13.457 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 105/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.855e-03, size: 288, ETA: 2:50:00
2025-08-28 11:35:16.564 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 105/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.855e-03, size: 512, ETA: 2:49:56
2025-08-28 11:35:19.743 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 105/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.854e-03, size: 512, ETA: 2:49:53
2025-08-28 11:35:22.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 105/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 7.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.854e-03, size: 352, ETA: 2:49:50
2025-08-28 11:35:26.303 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 105/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.853e-03, size: 448, ETA: 2:49:47
2025-08-28 11:35:29.589 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 105/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.853e-03, size: 448, ETA: 2:49:44
2025-08-28 11:35:31.079 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:35:37.446 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:35:38.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:35:39.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4950
2025-08-28 11:35:40.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4387
2025-08-28 11:35:40.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2597
2025-08-28 11:35:40.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3978
2025-08-28 11:35:40.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:35:40.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:35:40.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-08-28 11:35:40.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-08-28 11:35:40.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.260
2025-08-28 11:35:40.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.398
2025-08-28 11:35:40.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:35:40.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:35:40.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:35:40.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:35:40.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:35:40.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:35:40.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:35:40.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:35:40.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:35:41.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:35:42.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:35:43.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:35:45.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:35:46.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:35:47.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:35:48.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:35:50.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:35:51.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:35:51.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-28 11:35:51.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 11:35:51.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:35:51.214 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.95 ms, Average inference time: 7.08 ms

2025-08-28 11:35:51.215 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:35:51.292 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:35:51.432 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch106
2025-08-28 11:35:54.644 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 106/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.852e-03, size: 448, ETA: 2:49:39
2025-08-28 11:35:57.817 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 106/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.852e-03, size: 320, ETA: 2:49:36
2025-08-28 11:36:01.079 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 106/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.851e-03, size: 448, ETA: 2:49:33
2025-08-28 11:36:04.304 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 106/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.851e-03, size: 448, ETA: 2:49:30
2025-08-28 11:36:07.603 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 106/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.851e-03, size: 352, ETA: 2:49:27
2025-08-28 11:36:10.730 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 106/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.850e-03, size: 288, ETA: 2:49:23
2025-08-28 11:36:12.130 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:36:18.423 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:36:21.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:36:23.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5321
2025-08-28 11:36:23.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4241
2025-08-28 11:36:23.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3386
2025-08-28 11:36:23.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4316
2025-08-28 11:36:23.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:36:23.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:36:23.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-08-28 11:36:23.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-28 11:36:23.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.339
2025-08-28 11:36:23.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.432
2025-08-28 11:36:23.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:36:23.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:36:23.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:36:23.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:36:23.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:36:23.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:36:23.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:36:23.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:36:23.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:36:25.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:36:27.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:36:30.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:36:32.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:36:34.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:36:36.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:36:38.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:36:41.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:36:43.318 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:36:43.318 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:36:43.318 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 11:36:43.318 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:36:43.344 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.98 ms, Average inference time: 7.23 ms

2025-08-28 11:36:43.346 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:36:43.481 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:36:43.568 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch107
2025-08-28 11:36:46.700 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 107/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.849e-03, size: 544, ETA: 2:49:17
2025-08-28 11:36:49.958 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 107/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.849e-03, size: 320, ETA: 2:49:14
2025-08-28 11:36:53.121 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 107/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.849e-03, size: 352, ETA: 2:49:11
2025-08-28 11:36:56.305 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 107/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.848e-03, size: 480, ETA: 2:49:07
2025-08-28 11:36:59.632 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 107/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.848e-03, size: 352, ETA: 2:49:05
2025-08-28 11:37:02.731 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 107/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.847e-03, size: 256, ETA: 2:49:01
2025-08-28 11:37:04.125 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:37:10.267 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:37:11.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:37:12.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5350
2025-08-28 11:37:12.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4648
2025-08-28 11:37:12.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3183
2025-08-28 11:37:12.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4394
2025-08-28 11:37:12.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:37:12.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:37:12.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-08-28 11:37:12.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.465
2025-08-28 11:37:12.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-08-28 11:37:12.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.439
2025-08-28 11:37:12.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:37:12.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:37:12.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:37:12.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:37:12.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:37:12.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:37:12.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:37:12.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:37:12.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:37:13.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:37:15.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:37:16.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:37:17.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:37:18.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:37:19.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:37:20.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:37:21.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:37:22.961 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:37:22.961 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:37:22.961 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:37:22.961 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:37:22.969 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.94 ms, Average inference time: 7.23 ms

2025-08-28 11:37:22.970 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:37:23.051 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:37:23.139 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch108
2025-08-28 11:37:26.236 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 108/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.847e-03, size: 512, ETA: 2:48:55
2025-08-28 11:37:29.453 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 108/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.846e-03, size: 416, ETA: 2:48:52
2025-08-28 11:37:32.628 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 108/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.846e-03, size: 512, ETA: 2:48:48
2025-08-28 11:37:35.902 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 108/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.845e-03, size: 320, ETA: 2:48:45
2025-08-28 11:37:39.167 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 108/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 1.0, lr: 1.845e-03, size: 480, ETA: 2:48:42
2025-08-28 11:37:42.412 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 108/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.845e-03, size: 320, ETA: 2:48:39
2025-08-28 11:37:43.885 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:37:50.086 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:37:54.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:37:56.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5537
2025-08-28 11:37:57.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4815
2025-08-28 11:37:57.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3238
2025-08-28 11:37:57.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4530
2025-08-28 11:37:57.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:37:57.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:37:57.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.554
2025-08-28 11:37:57.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-28 11:37:57.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.324
2025-08-28 11:37:57.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.453
2025-08-28 11:37:57.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:37:57.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:37:57.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:37:57.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:37:57.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:37:57.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:37:57.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:37:57.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:37:57.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:38:00.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:38:03.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:38:07.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:38:10.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:38:13.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:38:17.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:38:20.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:38:23.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:38:26.741 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:38:26.742 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:38:26.742 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 11:38:26.742 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:38:26.767 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.99 ms, Average inference time: 7.24 ms

2025-08-28 11:38:26.769 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:38:26.841 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:38:26.924 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch109
2025-08-28 11:38:30.026 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 109/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.3, lr: 1.844e-03, size: 320, ETA: 2:48:34
2025-08-28 11:38:33.289 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 109/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.843e-03, size: 352, ETA: 2:48:31
2025-08-28 11:38:36.430 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 109/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 7.9, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.1, lr: 1.843e-03, size: 320, ETA: 2:48:27
2025-08-28 11:38:39.821 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 109/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.843e-03, size: 480, ETA: 2:48:25
2025-08-28 11:38:42.947 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 109/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.842e-03, size: 448, ETA: 2:48:21
2025-08-28 11:38:46.255 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 109/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.2, lr: 1.842e-03, size: 448, ETA: 2:48:18
2025-08-28 11:38:47.700 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:38:53.985 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:38:56.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:38:58.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4909
2025-08-28 11:38:58.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4322
2025-08-28 11:38:58.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3347
2025-08-28 11:38:58.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4192
2025-08-28 11:38:58.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:38:58.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:38:58.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-08-28 11:38:58.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.432
2025-08-28 11:38:58.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.335
2025-08-28 11:38:58.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.419
2025-08-28 11:38:58.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:38:58.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:38:58.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:38:58.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:38:58.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:38:58.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:38:58.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:38:58.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:38:58.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:39:00.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:39:02.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:39:04.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:39:06.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:39:08.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:39:10.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:39:12.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:39:14.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:39:16.523 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:39:16.523 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-28 11:39:16.523 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 11:39:16.523 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:39:16.549 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.99 ms, Average inference time: 7.15 ms

2025-08-28 11:39:16.551 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:39:16.727 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:39:16.848 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch110
2025-08-28 11:39:19.995 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 110/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.841e-03, size: 448, ETA: 2:48:13
2025-08-28 11:39:23.248 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 110/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.841e-03, size: 576, ETA: 2:48:10
2025-08-28 11:39:26.666 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 110/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.169s, data_time: 0.003s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.840e-03, size: 320, ETA: 2:48:08
2025-08-28 11:39:29.908 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 110/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.840e-03, size: 480, ETA: 2:48:05
2025-08-28 11:39:33.123 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 110/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.839e-03, size: 480, ETA: 2:48:01
2025-08-28 11:39:36.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 110/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.839e-03, size: 384, ETA: 2:47:58
2025-08-28 11:39:37.712 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:39:44.013 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:39:46.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:39:47.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5595
2025-08-28 11:39:48.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4580
2025-08-28 11:39:48.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3316
2025-08-28 11:39:48.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4497
2025-08-28 11:39:48.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:39:48.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:39:48.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.560
2025-08-28 11:39:48.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.458
2025-08-28 11:39:48.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.332
2025-08-28 11:39:48.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.450
2025-08-28 11:39:48.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:39:48.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:39:48.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:39:48.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:39:48.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:39:48.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:39:48.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:39:48.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:39:48.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:39:50.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:39:51.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:39:53.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:39:55.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:39:57.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:39:59.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:40:01.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:40:02.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:40:04.748 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:40:04.748 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:40:04.748 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 11:40:04.748 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:40:04.773 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.97 ms, Average inference time: 7.19 ms

2025-08-28 11:40:04.775 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:40:04.853 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:40:04.936 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch111
2025-08-28 11:40:07.956 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 111/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.838e-03, size: 256, ETA: 2:47:52
2025-08-28 11:40:11.304 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 111/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.838e-03, size: 384, ETA: 2:47:49
2025-08-28 11:40:14.595 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 111/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 7.7, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.837e-03, size: 320, ETA: 2:47:46
2025-08-28 11:40:17.746 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 111/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.837e-03, size: 256, ETA: 2:47:43
2025-08-28 11:40:20.988 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 111/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.836e-03, size: 288, ETA: 2:47:40
2025-08-28 11:40:24.419 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 111/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.9, lr: 1.836e-03, size: 416, ETA: 2:47:37
2025-08-28 11:40:25.869 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:40:32.170 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:40:33.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:40:34.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5158
2025-08-28 11:40:34.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4160
2025-08-28 11:40:34.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2320
2025-08-28 11:40:34.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3879
2025-08-28 11:40:34.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:40:34.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:40:34.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-08-28 11:40:34.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-08-28 11:40:34.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.232
2025-08-28 11:40:34.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.388
2025-08-28 11:40:34.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:40:34.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:40:34.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:40:34.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:40:34.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:40:34.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:40:34.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:40:34.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:40:34.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:40:36.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:40:37.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:40:38.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:40:39.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:40:40.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:40:42.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:40:43.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:40:44.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:40:45.604 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:40:45.604 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-28 11:40:45.604 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-28 11:40:45.604 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:40:45.612 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.96 ms, Average inference time: 7.15 ms

2025-08-28 11:40:45.613 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:40:45.691 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:40:45.784 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch112
2025-08-28 11:40:48.952 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 112/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.835e-03, size: 416, ETA: 2:47:33
2025-08-28 11:40:52.198 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 112/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.835e-03, size: 288, ETA: 2:47:29
2025-08-28 11:40:55.500 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 112/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.834e-03, size: 384, ETA: 2:47:27
2025-08-28 11:40:58.819 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 112/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.6, lr: 1.834e-03, size: 576, ETA: 2:47:24
2025-08-28 11:41:02.046 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 112/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.834e-03, size: 320, ETA: 2:47:20
2025-08-28 11:41:05.275 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 112/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.833e-03, size: 384, ETA: 2:47:17
2025-08-28 11:41:06.761 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:41:13.249 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:41:15.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:41:17.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5653
2025-08-28 11:41:17.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4740
2025-08-28 11:41:17.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2927
2025-08-28 11:41:17.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4440
2025-08-28 11:41:17.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:41:17.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:41:17.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.565
2025-08-28 11:41:17.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-28 11:41:17.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.293
2025-08-28 11:41:17.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.444
2025-08-28 11:41:17.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:41:17.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:41:17.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:41:17.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:41:17.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:41:17.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:41:17.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:41:17.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:41:17.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:41:19.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:41:21.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:41:23.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:41:25.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:41:27.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:41:29.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:41:30.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:41:32.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:41:34.803 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:41:34.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:41:34.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:41:34.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:41:34.828 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.96 ms, Average inference time: 7.11 ms

2025-08-28 11:41:34.837 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:41:34.916 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:41:34.999 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch113
2025-08-28 11:41:38.064 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 113/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.832e-03, size: 256, ETA: 2:47:12
2025-08-28 11:41:41.323 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 113/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.832e-03, size: 544, ETA: 2:47:09
2025-08-28 11:41:44.575 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 113/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.832e-03, size: 384, ETA: 2:47:06
2025-08-28 11:41:47.777 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 113/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.6, lr: 1.831e-03, size: 384, ETA: 2:47:03
2025-08-28 11:41:50.923 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 113/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.831e-03, size: 320, ETA: 2:46:59
2025-08-28 11:41:54.204 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 113/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.1, lr: 1.830e-03, size: 576, ETA: 2:46:56
2025-08-28 11:41:55.701 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:42:02.058 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:42:04.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:42:07.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5266
2025-08-28 11:42:07.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4670
2025-08-28 11:42:07.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3114
2025-08-28 11:42:07.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4350
2025-08-28 11:42:07.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:42:07.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:42:07.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-08-28 11:42:07.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.467
2025-08-28 11:42:07.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.311
2025-08-28 11:42:07.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.435
2025-08-28 11:42:07.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:42:07.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:42:07.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:42:07.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:42:07.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:42:07.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:42:07.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:42:07.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:42:07.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:42:10.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:42:12.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:42:15.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:42:17.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:42:20.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:42:22.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:42:25.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:42:27.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:42:30.460 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:42:30.460 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-28 11:42:30.460 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:42:30.460 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:42:30.488 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.98 ms, Average inference time: 7.16 ms

2025-08-28 11:42:30.489 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:42:30.569 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:42:30.656 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch114
2025-08-28 11:42:33.820 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 114/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.830e-03, size: 256, ETA: 2:46:51
2025-08-28 11:42:37.039 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 114/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.829e-03, size: 288, ETA: 2:46:48
2025-08-28 11:42:40.246 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 114/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.829e-03, size: 288, ETA: 2:46:45
2025-08-28 11:42:43.535 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 114/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.8, lr: 1.828e-03, size: 320, ETA: 2:46:42
2025-08-28 11:42:46.773 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 114/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.828e-03, size: 320, ETA: 2:46:39
2025-08-28 11:42:49.984 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 114/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.827e-03, size: 256, ETA: 2:46:35
2025-08-28 11:42:51.459 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:42:57.678 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:43:00.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:43:01.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5345
2025-08-28 11:43:02.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4823
2025-08-28 11:43:02.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3018
2025-08-28 11:43:02.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4395
2025-08-28 11:43:02.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:43:02.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:43:02.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-08-28 11:43:02.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-28 11:43:02.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.302
2025-08-28 11:43:02.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.440
2025-08-28 11:43:02.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:43:02.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:43:02.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:43:02.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:43:02.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:43:02.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:43:02.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:43:02.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:43:02.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:43:04.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:43:06.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:43:08.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:43:10.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:43:12.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:43:14.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:43:16.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:43:18.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:43:21.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:43:21.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 11:43:21.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:43:21.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:43:21.197 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.95 ms, Average inference time: 7.09 ms

2025-08-28 11:43:21.198 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:43:21.284 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:43:21.371 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch115
2025-08-28 11:43:24.511 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 115/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.827e-03, size: 416, ETA: 2:46:30
2025-08-28 11:43:27.772 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 115/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.826e-03, size: 480, ETA: 2:46:27
2025-08-28 11:43:31.051 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 115/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.161s, data_time: 0.005s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.826e-03, size: 416, ETA: 2:46:24
2025-08-28 11:43:34.263 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 115/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.825e-03, size: 288, ETA: 2:46:21
2025-08-28 11:43:37.425 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 115/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.825e-03, size: 480, ETA: 2:46:17
2025-08-28 11:43:40.717 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 115/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.824e-03, size: 512, ETA: 2:46:15
2025-08-28 11:43:42.163 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:43:48.473 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:43:50.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:43:51.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5518
2025-08-28 11:43:51.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4648
2025-08-28 11:43:52.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2998
2025-08-28 11:43:52.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4388
2025-08-28 11:43:52.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:43:52.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:43:52.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.552
2025-08-28 11:43:52.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.465
2025-08-28 11:43:52.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.300
2025-08-28 11:43:52.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.439
2025-08-28 11:43:52.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:43:52.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:43:52.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:43:52.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:43:52.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:43:52.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:43:52.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:43:52.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:43:52.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:43:53.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:43:55.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:43:56.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:43:58.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:43:59.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:44:01.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:44:03.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:44:04.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:44:06.139 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:44:06.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:44:06.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:44:06.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:44:06.163 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.97 ms, Average inference time: 7.18 ms

2025-08-28 11:44:06.165 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:44:06.244 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:44:06.334 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch116
2025-08-28 11:44:09.442 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 116/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.824e-03, size: 256, ETA: 2:46:09
2025-08-28 11:44:12.619 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 116/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.823e-03, size: 320, ETA: 2:46:06
2025-08-28 11:44:15.785 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 116/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.823e-03, size: 480, ETA: 2:46:02
2025-08-28 11:44:18.975 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 116/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.822e-03, size: 512, ETA: 2:45:59
2025-08-28 11:44:22.280 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 116/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.822e-03, size: 416, ETA: 2:45:56
2025-08-28 11:44:25.485 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 116/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.1, lr: 1.821e-03, size: 544, ETA: 2:45:53
2025-08-28 11:44:26.960 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:44:33.358 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:44:35.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:44:37.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5657
2025-08-28 11:44:37.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5073
2025-08-28 11:44:37.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3163
2025-08-28 11:44:37.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4631
2025-08-28 11:44:37.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:44:37.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:44:37.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.566
2025-08-28 11:44:37.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-08-28 11:44:37.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-28 11:44:37.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.463
2025-08-28 11:44:37.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:44:37.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:44:37.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:44:37.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:44:37.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:44:37.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:44:37.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:44:37.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:44:37.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:44:39.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:44:41.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:44:43.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:44:45.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:44:47.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:44:49.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:44:51.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:44:53.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:44:55.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:44:55.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 11:44:55.403 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 11:44:55.403 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:44:55.424 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.96 ms, Average inference time: 7.17 ms

2025-08-28 11:44:55.434 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:44:55.506 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:44:55.588 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch117
2025-08-28 11:44:58.621 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 117/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.821e-03, size: 416, ETA: 2:45:47
2025-08-28 11:45:01.956 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 117/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.820e-03, size: 544, ETA: 2:45:45
2025-08-28 11:45:05.244 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 117/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.820e-03, size: 480, ETA: 2:45:42
2025-08-28 11:45:08.427 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 117/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.819e-03, size: 544, ETA: 2:45:38
2025-08-28 11:45:11.606 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 117/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.819e-03, size: 384, ETA: 2:45:35
2025-08-28 11:45:14.758 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 117/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.818e-03, size: 416, ETA: 2:45:31
2025-08-28 11:45:16.170 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:45:22.352 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:45:24.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:45:25.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4786
2025-08-28 11:45:25.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4524
2025-08-28 11:45:26.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2680
2025-08-28 11:45:26.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3997
2025-08-28 11:45:26.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:45:26.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:45:26.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-08-28 11:45:26.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-28 11:45:26.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.268
2025-08-28 11:45:26.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.400
2025-08-28 11:45:26.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:45:26.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:45:26.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:45:26.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:45:26.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:45:26.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:45:26.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:45:26.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:45:26.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:45:27.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:45:29.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:45:30.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:45:32.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:45:34.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:45:35.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:45:37.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:45:39.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:45:40.712 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:45:40.712 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-28 11:45:40.712 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 11:45:40.712 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:45:40.739 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.95 ms, Average inference time: 7.11 ms

2025-08-28 11:45:40.741 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:45:40.820 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:45:40.906 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch118
2025-08-28 11:45:43.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 118/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.818e-03, size: 384, ETA: 2:45:26
2025-08-28 11:45:47.144 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 118/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.817e-03, size: 480, ETA: 2:45:22
2025-08-28 11:45:50.604 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 118/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.171s, data_time: 0.003s, total_loss: 6.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.817e-03, size: 480, ETA: 2:45:20
2025-08-28 11:45:53.963 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 118/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.9, lr: 1.816e-03, size: 544, ETA: 2:45:18
2025-08-28 11:45:57.291 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 118/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.1, lr: 1.816e-03, size: 416, ETA: 2:45:15
2025-08-28 11:46:00.426 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 118/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.815e-03, size: 352, ETA: 2:45:11
2025-08-28 11:46:01.808 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:46:08.060 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:46:09.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:46:10.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5324
2025-08-28 11:46:10.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4462
2025-08-28 11:46:10.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3287
2025-08-28 11:46:10.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4358
2025-08-28 11:46:10.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:46:10.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:46:10.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-08-28 11:46:10.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.446
2025-08-28 11:46:10.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.329
2025-08-28 11:46:10.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.436
2025-08-28 11:46:10.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:46:10.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:46:10.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:46:10.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:46:10.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:46:10.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:46:10.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:46:10.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:46:10.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:46:11.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:46:13.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:46:14.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:46:15.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:46:16.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:46:17.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:46:18.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:46:19.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:46:21.001 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:46:21.001 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:46:21.001 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:46:21.002 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:46:21.009 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.94 ms, Average inference time: 7.10 ms

2025-08-28 11:46:21.011 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:46:21.182 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:46:21.262 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch119
2025-08-28 11:46:24.411 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 119/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.815e-03, size: 480, ETA: 2:45:06
2025-08-28 11:46:27.623 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 119/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.814e-03, size: 544, ETA: 2:45:03
2025-08-28 11:46:30.935 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 119/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.814e-03, size: 448, ETA: 2:45:00
2025-08-28 11:46:34.154 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 119/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.813e-03, size: 288, ETA: 2:44:56
2025-08-28 11:46:37.277 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 119/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.813e-03, size: 448, ETA: 2:44:53
2025-08-28 11:46:40.544 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 119/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.812e-03, size: 480, ETA: 2:44:50
2025-08-28 11:46:42.006 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:46:48.288 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:46:50.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:46:51.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5004
2025-08-28 11:46:51.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4421
2025-08-28 11:46:51.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2641
2025-08-28 11:46:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4022
2025-08-28 11:46:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:46:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:46:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-08-28 11:46:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.442
2025-08-28 11:46:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.264
2025-08-28 11:46:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.402
2025-08-28 11:46:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:46:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:46:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:46:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:46:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:46:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:46:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:46:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:46:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:46:53.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:46:54.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:46:55.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:46:57.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:46:58.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:47:00.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:47:01.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:47:03.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:47:04.421 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:47:04.422 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 11:47:04.422 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 11:47:04.422 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:47:04.432 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.97 ms, Average inference time: 7.24 ms

2025-08-28 11:47:04.433 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:47:04.557 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:47:04.644 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch120
2025-08-28 11:47:07.935 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 120/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.812e-03, size: 288, ETA: 2:44:45
2025-08-28 11:47:11.135 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 120/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.811e-03, size: 320, ETA: 2:44:42
2025-08-28 11:47:14.330 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 120/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.811e-03, size: 544, ETA: 2:44:39
2025-08-28 11:47:17.603 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 120/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.810e-03, size: 576, ETA: 2:44:36
2025-08-28 11:47:20.956 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 120/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 1.810e-03, size: 576, ETA: 2:44:33
2025-08-28 11:47:24.384 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 120/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.7, lr: 1.809e-03, size: 256, ETA: 2:44:31
2025-08-28 11:47:25.767 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:47:32.080 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:47:36.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:47:38.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5493
2025-08-28 11:47:39.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4760
2025-08-28 11:47:39.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3269
2025-08-28 11:47:39.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4508
2025-08-28 11:47:39.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:47:39.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:47:39.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.549
2025-08-28 11:47:39.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-08-28 11:47:39.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.327
2025-08-28 11:47:39.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.451
2025-08-28 11:47:39.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:47:39.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:47:39.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:47:39.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:47:39.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:47:39.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:47:39.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:47:39.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:47:39.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:47:42.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:47:45.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:47:49.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:47:52.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:47:55.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:47:58.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:48:02.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:48:05.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:48:08.634 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:48:08.634 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:48:08.634 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 11:48:08.634 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:48:08.659 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 1.00 ms, Average inference time: 7.13 ms

2025-08-28 11:48:08.662 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:48:08.741 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:48:08.825 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch121
2025-08-28 11:48:12.001 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 121/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.809e-03, size: 480, ETA: 2:44:26
2025-08-28 11:48:15.202 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 121/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.808e-03, size: 544, ETA: 2:44:22
2025-08-28 11:48:18.417 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 121/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.808e-03, size: 416, ETA: 2:44:19
2025-08-28 11:48:21.651 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 121/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.807e-03, size: 480, ETA: 2:44:16
2025-08-28 11:48:24.987 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 121/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.807e-03, size: 576, ETA: 2:44:13
2025-08-28 11:48:28.153 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 121/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.806e-03, size: 416, ETA: 2:44:10
2025-08-28 11:48:29.674 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:48:35.746 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:48:37.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:48:38.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4977
2025-08-28 11:48:38.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4051
2025-08-28 11:48:38.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2821
2025-08-28 11:48:38.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3950
2025-08-28 11:48:38.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:48:38.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:48:38.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-28 11:48:38.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-08-28 11:48:38.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.282
2025-08-28 11:48:38.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.395
2025-08-28 11:48:38.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:48:38.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:48:38.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:48:38.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:48:38.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:48:38.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:48:38.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:48:38.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:48:38.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:48:40.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:48:41.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:48:42.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:48:44.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:48:45.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:48:47.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:48:48.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:48:49.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:48:51.122 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:48:51.122 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-28 11:48:51.122 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-28 11:48:51.123 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:48:51.130 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.96 ms, Average inference time: 7.19 ms

2025-08-28 11:48:51.132 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:48:51.264 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:48:51.349 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch122
2025-08-28 11:48:54.634 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 122/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.805e-03, size: 576, ETA: 2:44:06
2025-08-28 11:48:58.125 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 122/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.805e-03, size: 480, ETA: 2:44:03
2025-08-28 11:49:01.556 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 122/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.169s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.804e-03, size: 512, ETA: 2:44:01
2025-08-28 11:49:05.005 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 122/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.804e-03, size: 288, ETA: 2:43:58
2025-08-28 11:49:08.477 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 122/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.172s, data_time: 0.004s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.804e-03, size: 544, ETA: 2:43:56
2025-08-28 11:49:11.774 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 122/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.803e-03, size: 352, ETA: 2:43:53
2025-08-28 11:49:13.345 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:49:19.794 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:49:23.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:49:25.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5456
2025-08-28 11:49:26.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4812
2025-08-28 11:49:26.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3278
2025-08-28 11:49:26.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4516
2025-08-28 11:49:26.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:49:26.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:49:26.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.546
2025-08-28 11:49:26.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-28 11:49:26.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.328
2025-08-28 11:49:26.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.452
2025-08-28 11:49:26.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:49:26.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:49:26.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:49:26.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:49:26.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:49:26.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:49:26.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:49:26.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:49:26.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:49:29.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:49:32.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:49:35.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:49:37.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:49:40.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:49:43.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:49:46.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:49:49.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:49:52.302 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:49:52.302 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:49:52.302 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 11:49:52.302 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:49:52.328 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.34 ms, Average NMS time: 0.96 ms, Average inference time: 7.30 ms

2025-08-28 11:49:52.329 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:49:52.403 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:49:52.488 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch123
2025-08-28 11:49:55.557 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 123/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.802e-03, size: 416, ETA: 2:43:49
2025-08-28 11:49:58.808 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 123/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.802e-03, size: 288, ETA: 2:43:45
2025-08-28 11:50:02.099 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 123/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.162s, data_time: 0.006s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.801e-03, size: 448, ETA: 2:43:43
2025-08-28 11:50:05.234 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 123/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.801e-03, size: 256, ETA: 2:43:39
2025-08-28 11:50:08.457 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 123/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.800e-03, size: 320, ETA: 2:43:36
2025-08-28 11:50:11.605 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 123/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.800e-03, size: 512, ETA: 2:43:32
2025-08-28 11:50:13.102 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:50:19.401 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:50:21.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:50:21.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4980
2025-08-28 11:50:22.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4254
2025-08-28 11:50:22.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2680
2025-08-28 11:50:22.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3971
2025-08-28 11:50:22.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:50:22.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:50:22.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-28 11:50:22.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-08-28 11:50:22.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.268
2025-08-28 11:50:22.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.397
2025-08-28 11:50:22.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:50:22.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:50:22.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:50:22.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:50:22.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:50:22.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:50:22.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:50:22.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:50:22.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:50:23.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:50:24.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:50:26.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:50:27.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:50:28.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:50:29.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:50:31.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:50:32.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:50:33.497 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:50:33.497 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-28 11:50:33.497 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 11:50:33.497 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:50:33.505 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.97 ms, Average inference time: 7.17 ms

2025-08-28 11:50:33.507 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:50:33.581 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:50:33.669 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch124
2025-08-28 11:50:36.815 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 124/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.799e-03, size: 320, ETA: 2:43:27
2025-08-28 11:50:40.006 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 124/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.3, lr: 1.799e-03, size: 512, ETA: 2:43:24
2025-08-28 11:50:43.262 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 124/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.798e-03, size: 512, ETA: 2:43:21
2025-08-28 11:50:46.566 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 124/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.798e-03, size: 416, ETA: 2:43:18
2025-08-28 11:50:49.830 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 124/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.797e-03, size: 544, ETA: 2:43:15
2025-08-28 11:50:53.217 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 124/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.3, lr: 1.797e-03, size: 416, ETA: 2:43:12
2025-08-28 11:50:54.700 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:51:01.035 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:51:03.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:51:05.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5522
2025-08-28 11:51:05.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4707
2025-08-28 11:51:05.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3220
2025-08-28 11:51:05.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4483
2025-08-28 11:51:05.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:51:05.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:51:05.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.552
2025-08-28 11:51:05.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-08-28 11:51:05.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.322
2025-08-28 11:51:05.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.448
2025-08-28 11:51:05.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:51:05.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:51:05.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:51:05.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:51:05.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:51:05.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:51:05.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:51:05.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:51:05.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:51:07.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:51:09.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:51:11.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:51:13.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:51:15.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:51:17.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:51:20.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:51:22.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:51:24.048 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:51:24.049 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:51:24.049 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 11:51:24.049 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:51:24.085 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.96 ms, Average inference time: 7.20 ms

2025-08-28 11:51:24.087 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:51:24.215 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:51:24.297 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch125
2025-08-28 11:51:27.369 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 125/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.796e-03, size: 512, ETA: 2:43:07
2025-08-28 11:51:30.495 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 125/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.796e-03, size: 416, ETA: 2:43:03
2025-08-28 11:51:33.738 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 125/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.795e-03, size: 576, ETA: 2:43:00
2025-08-28 11:51:36.953 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 125/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.795e-03, size: 384, ETA: 2:42:57
2025-08-28 11:51:40.135 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 125/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.794e-03, size: 448, ETA: 2:42:54
2025-08-28 11:51:43.337 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 125/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.794e-03, size: 256, ETA: 2:42:50
2025-08-28 11:51:44.889 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:51:51.165 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:51:52.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:51:53.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4598
2025-08-28 11:51:53.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4444
2025-08-28 11:51:53.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1954
2025-08-28 11:51:53.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3665
2025-08-28 11:51:53.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:51:53.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:51:53.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-08-28 11:51:53.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-28 11:51:53.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.195
2025-08-28 11:51:53.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.367
2025-08-28 11:51:53.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:51:53.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:51:53.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:51:53.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:51:53.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:51:53.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:51:53.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:51:53.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:51:53.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:51:54.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:51:56.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:51:57.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:51:58.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:51:59.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:52:00.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:52:01.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:52:02.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:52:04.069 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:52:04.069 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-28 11:52:04.069 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.37
2025-08-28 11:52:04.069 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:52:04.076 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.92 ms, Average inference time: 7.11 ms

2025-08-28 11:52:04.078 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:52:04.162 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:52:04.253 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch126
2025-08-28 11:52:07.577 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 126/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.9, lr: 1.793e-03, size: 576, ETA: 2:42:46
2025-08-28 11:52:10.956 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 126/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.792e-03, size: 576, ETA: 2:42:44
2025-08-28 11:52:14.240 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 126/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.792e-03, size: 448, ETA: 2:42:41
2025-08-28 11:52:17.457 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 126/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.791e-03, size: 512, ETA: 2:42:37
2025-08-28 11:52:20.628 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 126/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.791e-03, size: 256, ETA: 2:42:34
2025-08-28 11:52:23.884 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 126/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.790e-03, size: 544, ETA: 2:42:31
2025-08-28 11:52:25.351 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:52:31.499 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:52:33.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:52:34.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5332
2025-08-28 11:52:35.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4597
2025-08-28 11:52:35.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3298
2025-08-28 11:52:35.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4409
2025-08-28 11:52:35.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:52:35.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:52:35.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-08-28 11:52:35.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-08-28 11:52:35.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.330
2025-08-28 11:52:35.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.441
2025-08-28 11:52:35.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:52:35.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:52:35.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:52:35.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:52:35.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:52:35.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:52:35.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:52:35.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:52:35.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:52:37.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:52:38.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:52:40.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:52:42.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:52:43.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:52:45.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:52:47.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:52:49.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:52:50.707 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:52:50.707 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:52:50.707 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:52:50.707 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:52:50.732 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.94 ms, Average inference time: 7.12 ms

2025-08-28 11:52:50.733 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:52:50.868 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:52:50.952 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch127
2025-08-28 11:52:54.132 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 127/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.790e-03, size: 256, ETA: 2:42:26
2025-08-28 11:52:57.385 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 127/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.789e-03, size: 256, ETA: 2:42:23
2025-08-28 11:53:00.545 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 127/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.789e-03, size: 512, ETA: 2:42:20
2025-08-28 11:53:03.712 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 127/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.788e-03, size: 288, ETA: 2:42:16
2025-08-28 11:53:06.830 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 127/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.788e-03, size: 288, ETA: 2:42:12
2025-08-28 11:53:09.945 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 127/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.787e-03, size: 288, ETA: 2:42:09
2025-08-28 11:53:11.321 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:53:17.573 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:53:19.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:53:20.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5117
2025-08-28 11:53:21.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4456
2025-08-28 11:53:21.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3002
2025-08-28 11:53:21.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4192
2025-08-28 11:53:21.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:53:21.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:53:21.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-08-28 11:53:21.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.446
2025-08-28 11:53:21.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.300
2025-08-28 11:53:21.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.419
2025-08-28 11:53:21.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:53:21.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:53:21.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:53:21.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:53:21.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:53:21.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:53:21.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:53:21.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:53:21.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:53:22.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:53:24.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:53:25.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:53:27.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:53:29.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:53:31.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:53:32.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:53:34.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:53:35.954 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:53:35.954 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:53:35.954 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 11:53:35.954 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:53:35.979 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.94 ms, Average inference time: 7.20 ms

2025-08-28 11:53:35.980 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:53:36.057 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:53:36.155 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch128
2025-08-28 11:53:39.219 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 128/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.786e-03, size: 480, ETA: 2:42:03
2025-08-28 11:53:42.413 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 128/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.786e-03, size: 352, ETA: 2:42:00
2025-08-28 11:53:45.679 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 128/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.785e-03, size: 352, ETA: 2:41:57
2025-08-28 11:53:48.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 128/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.785e-03, size: 384, ETA: 2:41:54
2025-08-28 11:53:52.338 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 128/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.784e-03, size: 320, ETA: 2:41:51
2025-08-28 11:53:55.567 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 128/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.784e-03, size: 576, ETA: 2:41:48
2025-08-28 11:53:57.161 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:54:03.437 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:54:05.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:54:06.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5413
2025-08-28 11:54:07.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4723
2025-08-28 11:54:07.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3377
2025-08-28 11:54:07.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4504
2025-08-28 11:54:07.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:54:07.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:54:07.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-08-28 11:54:07.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-08-28 11:54:07.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.338
2025-08-28 11:54:07.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.450
2025-08-28 11:54:07.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:54:07.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:54:07.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:54:07.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:54:07.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:54:07.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:54:07.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:54:07.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:54:07.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:54:09.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:54:10.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:54:12.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:54:14.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:54:16.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:54:17.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:54:19.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:54:21.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:54:22.948 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:54:22.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 11:54:22.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 11:54:22.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:54:22.976 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 1.00 ms, Average inference time: 7.22 ms

2025-08-28 11:54:22.977 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:54:23.059 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:54:23.193 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch129
2025-08-28 11:54:26.328 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 129/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.783e-03, size: 512, ETA: 2:41:43
2025-08-28 11:54:29.602 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 129/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.783e-03, size: 288, ETA: 2:41:40
2025-08-28 11:54:33.013 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 129/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.169s, data_time: 0.003s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.782e-03, size: 320, ETA: 2:41:38
2025-08-28 11:54:36.292 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 129/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.782e-03, size: 512, ETA: 2:41:35
2025-08-28 11:54:39.566 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 129/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.781e-03, size: 416, ETA: 2:41:32
2025-08-28 11:54:42.779 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 129/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.781e-03, size: 576, ETA: 2:41:29
2025-08-28 11:54:44.319 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:54:50.611 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:54:52.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:54:53.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5251
2025-08-28 11:54:53.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4374
2025-08-28 11:54:53.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3155
2025-08-28 11:54:53.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4260
2025-08-28 11:54:53.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:54:53.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:54:53.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 11:54:53.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-28 11:54:53.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-28 11:54:53.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.426
2025-08-28 11:54:53.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:54:53.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:54:53.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:54:53.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:54:53.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:54:53.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:54:53.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:54:53.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:54:53.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:54:54.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:54:56.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:54:57.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:54:58.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:55:00.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:55:01.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:55:02.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:55:03.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:55:05.181 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:55:05.181 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 11:55:05.181 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 11:55:05.181 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:55:05.189 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.07 ms, Average NMS time: 0.96 ms, Average inference time: 7.03 ms

2025-08-28 11:55:05.190 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:55:05.323 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:55:05.408 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch130
2025-08-28 11:55:08.604 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 130/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.780e-03, size: 352, ETA: 2:41:24
2025-08-28 11:55:11.864 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 130/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.779e-03, size: 416, ETA: 2:41:21
2025-08-28 11:55:15.137 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 130/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.779e-03, size: 480, ETA: 2:41:18
2025-08-28 11:55:18.368 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 130/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.3, cls_loss: 1.2, lr: 1.778e-03, size: 576, ETA: 2:41:15
2025-08-28 11:55:21.698 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 130/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.6, lr: 1.778e-03, size: 544, ETA: 2:41:12
2025-08-28 11:55:24.977 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 130/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.777e-03, size: 384, ETA: 2:41:09
2025-08-28 11:55:26.417 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:55:32.654 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:55:35.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:55:37.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5391
2025-08-28 11:55:37.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4477
2025-08-28 11:55:37.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3145
2025-08-28 11:55:37.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4338
2025-08-28 11:55:37.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:55:37.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:55:37.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-08-28 11:55:37.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.448
2025-08-28 11:55:37.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-08-28 11:55:37.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-08-28 11:55:37.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:55:37.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:55:37.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:55:37.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:55:37.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:55:37.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:55:37.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:55:37.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:55:37.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:55:39.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:55:41.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:55:43.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:55:45.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:55:47.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:55:50.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:55:52.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:55:54.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:55:56.760 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:55:56.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:55:56.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 11:55:56.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:55:56.788 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.99 ms, Average inference time: 7.11 ms

2025-08-28 11:55:56.789 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:55:56.865 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:55:56.948 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch131
2025-08-28 11:56:00.340 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 131/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.777e-03, size: 576, ETA: 2:41:05
2025-08-28 11:56:03.629 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 131/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.776e-03, size: 480, ETA: 2:41:02
2025-08-28 11:56:06.970 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 131/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.165s, data_time: 0.006s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.776e-03, size: 384, ETA: 2:40:59
2025-08-28 11:56:10.277 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 131/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.775e-03, size: 448, ETA: 2:40:56
2025-08-28 11:56:13.566 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 131/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.775e-03, size: 480, ETA: 2:40:53
2025-08-28 11:56:16.721 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 131/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.774e-03, size: 480, ETA: 2:40:50
2025-08-28 11:56:18.171 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:56:24.343 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:56:27.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:56:29.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5510
2025-08-28 11:56:29.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4834
2025-08-28 11:56:29.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3069
2025-08-28 11:56:29.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4471
2025-08-28 11:56:29.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:56:29.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:56:29.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-08-28 11:56:29.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.483
2025-08-28 11:56:29.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.307
2025-08-28 11:56:29.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.447
2025-08-28 11:56:29.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:56:29.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:56:29.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:56:29.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:56:29.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:56:29.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:56:29.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:56:29.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:56:29.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:56:32.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:56:34.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:56:36.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:56:38.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:56:41.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:56:43.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:56:45.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:56:48.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:56:50.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:56:50.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 11:56:50.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 11:56:50.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:56:50.398 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.97 ms, Average inference time: 7.22 ms

2025-08-28 11:56:50.399 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:56:50.479 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:56:50.566 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch132
2025-08-28 11:56:53.601 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 132/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.1, lr: 1.773e-03, size: 320, ETA: 2:40:44
2025-08-28 11:56:56.748 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 132/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.773e-03, size: 288, ETA: 2:40:41
2025-08-28 11:57:00.212 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 132/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.171s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.772e-03, size: 480, ETA: 2:40:39
2025-08-28 11:57:03.447 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 132/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.772e-03, size: 288, ETA: 2:40:35
2025-08-28 11:57:06.717 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 132/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.771e-03, size: 416, ETA: 2:40:32
2025-08-28 11:57:09.989 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 132/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 9.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 4.9, cls_loss: 1.1, lr: 1.771e-03, size: 448, ETA: 2:40:29
2025-08-28 11:57:11.404 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:57:17.862 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:57:20.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:57:22.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5196
2025-08-28 11:57:23.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4516
2025-08-28 11:57:23.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3010
2025-08-28 11:57:23.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4240
2025-08-28 11:57:23.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:57:23.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:57:23.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-08-28 11:57:23.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-28 11:57:23.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.301
2025-08-28 11:57:23.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.424
2025-08-28 11:57:23.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:57:23.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:57:23.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:57:23.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:57:23.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:57:23.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:57:23.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:57:23.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:57:23.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:57:25.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:57:28.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:57:30.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:57:32.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:57:35.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:57:37.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:57:40.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:57:42.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:57:45.106 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:57:45.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 11:57:45.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 11:57:45.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:57:45.132 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.99 ms, Average inference time: 7.17 ms

2025-08-28 11:57:45.134 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:57:45.214 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:57:45.296 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch133
2025-08-28 11:57:48.409 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 133/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.770e-03, size: 320, ETA: 2:40:24
2025-08-28 11:57:51.711 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 133/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.769e-03, size: 320, ETA: 2:40:21
2025-08-28 11:57:55.002 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 133/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.769e-03, size: 288, ETA: 2:40:18
2025-08-28 11:57:58.260 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 133/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.768e-03, size: 544, ETA: 2:40:15
2025-08-28 11:58:01.544 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 133/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 1.0, lr: 1.768e-03, size: 512, ETA: 2:40:12
2025-08-28 11:58:04.780 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 133/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.767e-03, size: 384, ETA: 2:40:09
2025-08-28 11:58:06.240 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:58:12.525 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:58:15.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:58:17.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5334
2025-08-28 11:58:17.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4339
2025-08-28 11:58:17.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2990
2025-08-28 11:58:17.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4221
2025-08-28 11:58:17.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:58:17.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:58:17.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-08-28 11:58:17.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.434
2025-08-28 11:58:17.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.299
2025-08-28 11:58:17.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.422
2025-08-28 11:58:17.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:58:17.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:58:17.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:58:17.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:58:17.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:58:17.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:58:17.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:58:17.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:58:17.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:58:19.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:58:22.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:58:24.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:58:26.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:58:28.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:58:31.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:58:33.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:58:35.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:58:37.963 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:58:37.963 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 11:58:37.963 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 11:58:37.963 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:58:37.991 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.98 ms, Average inference time: 7.11 ms

2025-08-28 11:58:37.993 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:58:38.082 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:58:38.170 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch134
2025-08-28 11:58:41.442 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 134/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.767e-03, size: 576, ETA: 2:40:04
2025-08-28 11:58:44.824 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 134/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.1, lr: 1.766e-03, size: 512, ETA: 2:40:02
2025-08-28 11:58:48.158 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 134/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.766e-03, size: 576, ETA: 2:39:59
2025-08-28 11:58:51.469 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 134/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.765e-03, size: 512, ETA: 2:39:56
2025-08-28 11:58:54.680 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 134/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.765e-03, size: 384, ETA: 2:39:53
2025-08-28 11:58:57.817 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 134/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.764e-03, size: 480, ETA: 2:39:49
2025-08-28 11:58:59.199 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:59:05.397 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 11:59:08.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 11:59:10.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5297
2025-08-28 11:59:10.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4711
2025-08-28 11:59:10.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3219
2025-08-28 11:59:10.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4409
2025-08-28 11:59:10.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 11:59:10.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 11:59:10.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 11:59:10.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-08-28 11:59:10.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.322
2025-08-28 11:59:10.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.441
2025-08-28 11:59:10.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 11:59:10.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 11:59:10.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 11:59:10.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 11:59:10.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 11:59:10.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 11:59:10.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 11:59:10.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 11:59:10.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 11:59:13.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 11:59:15.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 11:59:18.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 11:59:20.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 11:59:23.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 11:59:25.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 11:59:28.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 11:59:30.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 11:59:33.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 11:59:33.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 11:59:33.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 11:59:33.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 11:59:33.279 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.93 ms, Average inference time: 7.17 ms

2025-08-28 11:59:33.280 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:59:33.355 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 11:59:33.439 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch135
2025-08-28 11:59:36.494 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 135/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.3, lr: 1.763e-03, size: 256, ETA: 2:39:44
2025-08-28 11:59:39.744 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 135/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.2, cls_loss: 0.9, lr: 1.763e-03, size: 544, ETA: 2:39:40
2025-08-28 11:59:43.015 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 135/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.161s, data_time: 0.005s, total_loss: 7.1, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.762e-03, size: 320, ETA: 2:39:37
2025-08-28 11:59:46.155 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 135/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.762e-03, size: 256, ETA: 2:39:34
2025-08-28 11:59:49.390 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 135/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.761e-03, size: 576, ETA: 2:39:31
2025-08-28 11:59:52.797 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 135/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.761e-03, size: 288, ETA: 2:39:28
2025-08-28 11:59:54.192 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:00:00.557 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:00:02.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:00:04.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5398
2025-08-28 12:00:04.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4373
2025-08-28 12:00:04.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3397
2025-08-28 12:00:04.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4389
2025-08-28 12:00:04.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:00:04.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:00:04.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.540
2025-08-28 12:00:04.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-28 12:00:04.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-08-28 12:00:04.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.439
2025-08-28 12:00:04.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:00:04.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:00:04.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:00:04.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:00:04.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:00:04.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:00:04.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:00:04.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:00:04.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:00:06.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:00:08.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:00:10.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:00:11.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:00:13.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:00:15.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:00:17.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:00:18.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:00:20.667 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:00:20.667 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:00:20.667 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:00:20.667 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:00:20.695 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.96 ms, Average inference time: 7.11 ms

2025-08-28 12:00:20.695 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:00:20.775 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:00:20.894 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch136
2025-08-28 12:00:24.037 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 136/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.760e-03, size: 288, ETA: 2:39:23
2025-08-28 12:00:27.346 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 136/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.759e-03, size: 352, ETA: 2:39:20
2025-08-28 12:00:30.836 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 136/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.5Gb, iter_time: 0.172s, data_time: 0.006s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.759e-03, size: 544, ETA: 2:39:18
2025-08-28 12:00:34.214 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 136/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.758e-03, size: 320, ETA: 2:39:15
2025-08-28 12:00:37.404 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 136/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.9, lr: 1.758e-03, size: 480, ETA: 2:39:11
2025-08-28 12:00:40.669 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 136/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.757e-03, size: 320, ETA: 2:39:08
2025-08-28 12:00:42.173 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:00:48.248 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:00:49.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:00:51.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5540
2025-08-28 12:00:51.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4680
2025-08-28 12:00:51.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3163
2025-08-28 12:00:51.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4461
2025-08-28 12:00:51.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:00:51.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:00:51.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.554
2025-08-28 12:00:51.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-08-28 12:00:51.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-28 12:00:51.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.446
2025-08-28 12:00:51.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:00:51.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:00:51.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:00:51.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:00:51.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:00:51.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:00:51.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:00:51.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:00:51.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:00:52.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:00:53.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:00:55.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:00:56.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:00:57.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:00:59.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:01:00.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:01:01.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:01:03.326 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:01:03.327 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 12:01:03.327 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 12:01:03.327 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:01:03.336 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.95 ms, Average inference time: 7.11 ms

2025-08-28 12:01:03.338 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:01:03.411 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:01:03.492 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch137
2025-08-28 12:01:06.652 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 137/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.756e-03, size: 448, ETA: 2:39:04
2025-08-28 12:01:09.949 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 137/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.756e-03, size: 544, ETA: 2:39:01
2025-08-28 12:01:13.287 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 137/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.755e-03, size: 256, ETA: 2:38:58
2025-08-28 12:01:16.521 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 137/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.2, lr: 1.755e-03, size: 352, ETA: 2:38:55
2025-08-28 12:01:19.761 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 137/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.754e-03, size: 352, ETA: 2:38:51
2025-08-28 12:01:23.062 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 137/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.754e-03, size: 480, ETA: 2:38:48
2025-08-28 12:01:24.471 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:01:30.702 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:01:33.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:01:34.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5345
2025-08-28 12:01:35.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4631
2025-08-28 12:01:35.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2999
2025-08-28 12:01:35.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4325
2025-08-28 12:01:35.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:01:35.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:01:35.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.534
2025-08-28 12:01:35.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.463
2025-08-28 12:01:35.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.300
2025-08-28 12:01:35.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.432
2025-08-28 12:01:35.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:01:35.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:01:35.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:01:35.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:01:35.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:01:35.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:01:35.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:01:35.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:01:35.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:01:37.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:01:39.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:01:41.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:01:43.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:01:45.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:01:47.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:01:50.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:01:52.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:01:54.198 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:01:54.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:01:54.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:01:54.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:01:54.228 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.94 ms, Average inference time: 7.06 ms

2025-08-28 12:01:54.229 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:01:54.305 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:01:54.390 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch138
2025-08-28 12:01:57.511 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 138/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.753e-03, size: 320, ETA: 2:38:43
2025-08-28 12:02:00.695 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 138/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.752e-03, size: 384, ETA: 2:38:40
2025-08-28 12:02:03.959 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 138/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.752e-03, size: 352, ETA: 2:38:37
2025-08-28 12:02:07.162 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 138/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 1.751e-03, size: 576, ETA: 2:38:34
2025-08-28 12:02:10.635 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 138/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.751e-03, size: 256, ETA: 2:38:31
2025-08-28 12:02:13.779 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 138/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.750e-03, size: 288, ETA: 2:38:28
2025-08-28 12:02:15.256 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:02:21.401 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:02:27.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:02:30.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5431
2025-08-28 12:02:31.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4326
2025-08-28 12:02:31.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3270
2025-08-28 12:02:31.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4342
2025-08-28 12:02:31.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:02:31.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:02:31.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-08-28 12:02:31.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.433
2025-08-28 12:02:31.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.327
2025-08-28 12:02:31.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-08-28 12:02:31.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:02:31.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:02:31.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:02:31.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:02:31.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:02:31.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:02:31.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:02:31.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:02:31.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:02:36.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:02:41.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:02:45.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:02:50.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:02:54.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:02:59.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:03:03.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:03:08.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:03:13.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:03:13.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:03:13.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:03:13.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:03:13.225 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.97 ms, Average inference time: 7.14 ms

2025-08-28 12:03:13.226 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:03:13.320 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:03:13.403 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch139
2025-08-28 12:03:16.630 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 139/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.750e-03, size: 288, ETA: 2:38:23
2025-08-28 12:03:19.836 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 139/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.749e-03, size: 544, ETA: 2:38:20
2025-08-28 12:03:23.183 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 139/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.1, lr: 1.748e-03, size: 512, ETA: 2:38:17
2025-08-28 12:03:26.407 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 139/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.2, lr: 1.748e-03, size: 544, ETA: 2:38:14
2025-08-28 12:03:29.734 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 139/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.747e-03, size: 544, ETA: 2:38:11
2025-08-28 12:03:33.075 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 139/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.747e-03, size: 384, ETA: 2:38:08
2025-08-28 12:03:34.487 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:03:40.590 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:03:44.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:03:46.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5451
2025-08-28 12:03:46.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4488
2025-08-28 12:03:46.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3090
2025-08-28 12:03:46.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4343
2025-08-28 12:03:46.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:03:46.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:03:46.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-08-28 12:03:46.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-08-28 12:03:46.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.309
2025-08-28 12:03:46.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-08-28 12:03:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:03:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:03:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:03:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:03:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:03:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:03:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:03:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:03:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:03:49.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:03:52.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:03:55.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:03:57.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:04:00.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:04:03.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:04:06.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:04:09.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:04:11.882 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:04:11.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:04:11.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:04:11.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:04:11.908 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 1.03 ms, Average inference time: 7.17 ms

2025-08-28 12:04:11.909 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:04:11.984 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:04:12.112 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch140
2025-08-28 12:04:15.171 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 140/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 8.4, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.5, lr: 1.746e-03, size: 256, ETA: 2:38:03
2025-08-28 12:04:18.328 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 140/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.746e-03, size: 480, ETA: 2:37:59
2025-08-28 12:04:21.537 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 140/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.745e-03, size: 320, ETA: 2:37:56
2025-08-28 12:04:24.751 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 140/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.744e-03, size: 576, ETA: 2:37:53
2025-08-28 12:04:28.133 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 140/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.744e-03, size: 480, ETA: 2:37:50
2025-08-28 12:04:31.402 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 140/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.743e-03, size: 512, ETA: 2:37:47
2025-08-28 12:04:32.840 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:04:39.080 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:04:42.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:04:44.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5436
2025-08-28 12:04:45.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4561
2025-08-28 12:04:45.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2887
2025-08-28 12:04:45.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4295
2025-08-28 12:04:45.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:04:45.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:04:45.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.544
2025-08-28 12:04:45.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.456
2025-08-28 12:04:45.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.289
2025-08-28 12:04:45.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.429
2025-08-28 12:04:45.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:04:45.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:04:45.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:04:45.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:04:45.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:04:45.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:04:45.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:04:45.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:04:45.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:04:47.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:04:50.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:04:53.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:04:56.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:04:59.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:05:01.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:05:04.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:05:07.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:05:10.079 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:05:10.079 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:05:10.079 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:05:10.080 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:05:10.105 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.95 ms, Average inference time: 7.06 ms

2025-08-28 12:05:10.106 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:05:10.186 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:05:10.269 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch141
2025-08-28 12:05:13.300 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 141/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.743e-03, size: 352, ETA: 2:37:41
2025-08-28 12:05:16.460 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 141/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.742e-03, size: 288, ETA: 2:37:38
2025-08-28 12:05:19.698 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 141/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.742e-03, size: 416, ETA: 2:37:35
2025-08-28 12:05:22.957 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 141/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.741e-03, size: 448, ETA: 2:37:32
2025-08-28 12:05:26.500 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 141/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.176s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.740e-03, size: 544, ETA: 2:37:30
2025-08-28 12:05:29.715 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 141/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.740e-03, size: 384, ETA: 2:37:26
2025-08-28 12:05:31.174 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:05:37.497 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:05:39.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:05:40.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5309
2025-08-28 12:05:40.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4735
2025-08-28 12:05:40.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2971
2025-08-28 12:05:40.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4338
2025-08-28 12:05:40.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:05:40.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:05:40.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-08-28 12:05:40.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-28 12:05:40.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.297
2025-08-28 12:05:40.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-08-28 12:05:40.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:05:40.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:05:40.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:05:40.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:05:40.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:05:40.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:05:40.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:05:40.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:05:40.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:05:42.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:05:43.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:05:45.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:05:46.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:05:48.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:05:49.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:05:51.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:05:52.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:05:53.971 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:05:53.971 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:05:53.971 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:05:53.971 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:05:53.981 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.97 ms, Average inference time: 7.11 ms

2025-08-28 12:05:53.988 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:05:54.068 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:05:54.155 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch142
2025-08-28 12:05:57.474 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 142/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 0.9, lr: 1.739e-03, size: 448, ETA: 2:37:22
2025-08-28 12:06:00.791 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 142/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.739e-03, size: 384, ETA: 2:37:19
2025-08-28 12:06:04.060 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 142/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.9, lr: 1.738e-03, size: 448, ETA: 2:37:16
2025-08-28 12:06:07.312 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 142/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.737e-03, size: 480, ETA: 2:37:13
2025-08-28 12:06:10.625 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 142/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.737e-03, size: 480, ETA: 2:37:10
2025-08-28 12:06:13.824 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 142/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.736e-03, size: 256, ETA: 2:37:07
2025-08-28 12:06:15.237 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:06:21.525 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:06:24.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:06:26.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5276
2025-08-28 12:06:26.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4687
2025-08-28 12:06:26.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3158
2025-08-28 12:06:26.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4374
2025-08-28 12:06:26.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:06:26.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:06:26.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-08-28 12:06:26.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-08-28 12:06:26.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-28 12:06:26.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.437
2025-08-28 12:06:26.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:06:26.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:06:26.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:06:26.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:06:26.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:06:26.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:06:26.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:06:26.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:06:26.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:06:28.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:06:31.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:06:33.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:06:35.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:06:38.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:06:40.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:06:42.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:06:44.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:06:47.167 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:06:47.168 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:06:47.168 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:06:47.168 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:06:47.193 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.98 ms, Average inference time: 7.24 ms

2025-08-28 12:06:47.195 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:06:47.280 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:06:47.363 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch143
2025-08-28 12:06:50.460 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 143/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.4, cls_loss: 1.0, lr: 1.736e-03, size: 512, ETA: 2:37:01
2025-08-28 12:06:53.791 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 143/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.735e-03, size: 576, ETA: 2:36:58
2025-08-28 12:06:57.157 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 143/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.734e-03, size: 384, ETA: 2:36:56
2025-08-28 12:07:00.434 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 143/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.734e-03, size: 448, ETA: 2:36:53
2025-08-28 12:07:03.624 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 143/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.733e-03, size: 416, ETA: 2:36:49
2025-08-28 12:07:06.875 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 143/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.733e-03, size: 512, ETA: 2:36:46
2025-08-28 12:07:08.319 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:07:14.558 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:07:16.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:07:18.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5294
2025-08-28 12:07:18.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4722
2025-08-28 12:07:18.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2695
2025-08-28 12:07:18.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4237
2025-08-28 12:07:18.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:07:18.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:07:18.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-08-28 12:07:18.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-08-28 12:07:18.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.269
2025-08-28 12:07:18.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.424
2025-08-28 12:07:18.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:07:18.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:07:18.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:07:18.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:07:18.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:07:18.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:07:18.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:07:18.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:07:18.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:07:20.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:07:22.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:07:23.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:07:25.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:07:27.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:07:29.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:07:30.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:07:32.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:07:34.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:07:34.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:07:34.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 12:07:34.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:07:34.543 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.96 ms, Average inference time: 7.16 ms

2025-08-28 12:07:34.544 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:07:34.623 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:07:34.709 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch144
2025-08-28 12:07:37.817 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 144/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.732e-03, size: 480, ETA: 2:36:41
2025-08-28 12:07:41.135 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 144/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 7.7, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.731e-03, size: 384, ETA: 2:36:38
2025-08-28 12:07:44.489 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 144/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.731e-03, size: 512, ETA: 2:36:35
2025-08-28 12:07:47.712 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 144/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.730e-03, size: 512, ETA: 2:36:32
2025-08-28 12:07:50.868 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 144/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.730e-03, size: 512, ETA: 2:36:29
2025-08-28 12:07:54.043 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 144/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.729e-03, size: 256, ETA: 2:36:25
2025-08-28 12:07:55.451 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:08:01.684 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:08:03.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:08:05.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5475
2025-08-28 12:08:05.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4445
2025-08-28 12:08:05.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2908
2025-08-28 12:08:05.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4276
2025-08-28 12:08:05.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:08:05.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:08:05.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.548
2025-08-28 12:08:05.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-28 12:08:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.291
2025-08-28 12:08:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.428
2025-08-28 12:08:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:08:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:08:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:08:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:08:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:08:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:08:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:08:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:08:05.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:08:07.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:08:09.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:08:10.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:08:12.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:08:14.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:08:16.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:08:17.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:08:19.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:08:21.323 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:08:21.323 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:08:21.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:08:21.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:08:21.351 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.99 ms, Average inference time: 7.21 ms

2025-08-28 12:08:21.352 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:08:21.434 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:08:21.520 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch145
2025-08-28 12:08:24.647 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 145/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.728e-03, size: 384, ETA: 2:36:20
2025-08-28 12:08:27.932 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 145/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.2, lr: 1.728e-03, size: 448, ETA: 2:36:17
2025-08-28 12:08:31.204 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 145/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.727e-03, size: 352, ETA: 2:36:14
2025-08-28 12:08:34.530 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 145/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.727e-03, size: 480, ETA: 2:36:11
2025-08-28 12:08:37.764 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 145/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.726e-03, size: 512, ETA: 2:36:08
2025-08-28 12:08:41.048 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 145/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.726e-03, size: 544, ETA: 2:36:05
2025-08-28 12:08:42.496 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:08:48.680 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:08:51.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:08:54.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5159
2025-08-28 12:08:54.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4371
2025-08-28 12:08:54.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3033
2025-08-28 12:08:54.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4188
2025-08-28 12:08:54.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:08:54.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:08:54.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-08-28 12:08:54.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-08-28 12:08:54.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-08-28 12:08:54.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.419
2025-08-28 12:08:54.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:08:54.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:08:54.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:08:54.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:08:54.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:08:54.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:08:54.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:08:54.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:08:54.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:08:57.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:09:00.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:09:02.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:09:05.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:09:08.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:09:10.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:09:13.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:09:16.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:09:18.948 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:09:18.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:09:18.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 12:09:18.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:09:18.976 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.97 ms, Average inference time: 7.15 ms

2025-08-28 12:09:18.977 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:09:19.064 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:09:19.153 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch146
2025-08-28 12:09:22.348 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 146/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.725e-03, size: 352, ETA: 2:36:00
2025-08-28 12:09:25.737 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 146/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.724e-03, size: 288, ETA: 2:35:57
2025-08-28 12:09:28.852 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 146/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.724e-03, size: 544, ETA: 2:35:54
2025-08-28 12:09:32.106 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 146/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.723e-03, size: 416, ETA: 2:35:51
2025-08-28 12:09:35.486 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 146/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.723e-03, size: 512, ETA: 2:35:48
2025-08-28 12:09:38.695 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 146/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.722e-03, size: 512, ETA: 2:35:45
2025-08-28 12:09:40.180 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:09:46.328 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:09:49.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:09:52.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5471
2025-08-28 12:09:52.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4304
2025-08-28 12:09:52.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3471
2025-08-28 12:09:52.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4415
2025-08-28 12:09:52.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:09:52.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:09:52.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.547
2025-08-28 12:09:52.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.430
2025-08-28 12:09:52.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-08-28 12:09:52.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.442
2025-08-28 12:09:52.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:09:52.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:09:52.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:09:52.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:09:52.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:09:52.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:09:52.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:09:52.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:09:52.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:09:55.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:09:57.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:10:00.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:10:03.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:10:06.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:10:09.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:10:11.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:10:14.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:10:17.371 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:10:17.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:10:17.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:10:17.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:10:17.397 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.97 ms, Average inference time: 7.23 ms

2025-08-28 12:10:17.398 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:10:17.481 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:10:17.567 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch147
2025-08-28 12:10:20.632 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 147/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.721e-03, size: 288, ETA: 2:35:40
2025-08-28 12:10:23.731 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 147/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.721e-03, size: 416, ETA: 2:35:36
2025-08-28 12:10:26.946 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 147/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.720e-03, size: 544, ETA: 2:35:33
2025-08-28 12:10:30.248 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 147/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.720e-03, size: 320, ETA: 2:35:30
2025-08-28 12:10:33.422 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 147/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.719e-03, size: 416, ETA: 2:35:27
2025-08-28 12:10:36.695 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 147/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.718e-03, size: 416, ETA: 2:35:23
2025-08-28 12:10:38.203 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:10:44.470 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:10:45.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:10:46.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5220
2025-08-28 12:10:46.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4401
2025-08-28 12:10:46.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3395
2025-08-28 12:10:46.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4339
2025-08-28 12:10:46.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:10:46.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:10:46.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-08-28 12:10:46.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.440
2025-08-28 12:10:46.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-08-28 12:10:46.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-08-28 12:10:46.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:10:46.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:10:46.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:10:46.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:10:46.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:10:46.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:10:46.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:10:46.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:10:46.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:10:48.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:10:49.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:10:50.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:10:51.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:10:52.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:10:53.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:10:54.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:10:55.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:10:56.215 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:10:56.216 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 12:10:56.216 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:10:56.216 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:10:56.224 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.94 ms, Average inference time: 7.16 ms

2025-08-28 12:10:56.226 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:10:56.306 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:10:56.395 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch148
2025-08-28 12:10:59.754 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 148/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 0.9, lr: 1.718e-03, size: 576, ETA: 2:35:19
2025-08-28 12:11:03.145 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 148/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.167s, data_time: 0.003s, total_loss: 6.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.717e-03, size: 544, ETA: 2:35:17
2025-08-28 12:11:06.525 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 148/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.716e-03, size: 448, ETA: 2:35:14
2025-08-28 12:11:09.850 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 148/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.716e-03, size: 576, ETA: 2:35:11
2025-08-28 12:11:13.222 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 148/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.167s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.715e-03, size: 288, ETA: 2:35:08
2025-08-28 12:11:16.486 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 148/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 1.0, lr: 1.715e-03, size: 384, ETA: 2:35:05
2025-08-28 12:11:17.930 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:11:24.228 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:11:26.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:11:28.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5473
2025-08-28 12:11:28.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4848
2025-08-28 12:11:28.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3058
2025-08-28 12:11:28.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4460
2025-08-28 12:11:28.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:11:28.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:11:28.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.547
2025-08-28 12:11:28.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-08-28 12:11:28.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.306
2025-08-28 12:11:28.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.446
2025-08-28 12:11:28.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:11:28.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:11:28.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:11:28.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:11:28.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:11:28.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:11:28.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:11:28.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:11:28.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:11:30.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:11:32.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:11:34.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:11:36.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:11:38.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:11:40.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:11:42.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:11:44.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:11:46.259 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:11:46.259 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:11:46.259 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 12:11:46.259 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:11:46.290 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 1.02 ms, Average inference time: 7.23 ms

2025-08-28 12:11:46.291 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:11:46.415 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:11:46.508 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch149
2025-08-28 12:11:49.557 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 149/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.714e-03, size: 256, ETA: 2:35:00
2025-08-28 12:11:52.740 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 149/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.713e-03, size: 320, ETA: 2:34:56
2025-08-28 12:11:55.957 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 149/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.713e-03, size: 384, ETA: 2:34:53
2025-08-28 12:11:59.143 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 149/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.712e-03, size: 416, ETA: 2:34:50
2025-08-28 12:12:02.408 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 149/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.712e-03, size: 352, ETA: 2:34:47
2025-08-28 12:12:05.574 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 149/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.711e-03, size: 576, ETA: 2:34:43
2025-08-28 12:12:07.115 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:12:13.349 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:12:15.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:12:16.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5082
2025-08-28 12:12:17.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4514
2025-08-28 12:12:17.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2913
2025-08-28 12:12:17.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4170
2025-08-28 12:12:17.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:12:17.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:12:17.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.508
2025-08-28 12:12:17.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-28 12:12:17.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.291
2025-08-28 12:12:17.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.417
2025-08-28 12:12:17.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:12:17.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:12:17.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:12:17.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:12:17.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:12:17.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:12:17.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:12:17.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:12:17.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:12:19.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:12:20.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:12:22.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:12:24.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:12:26.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:12:27.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:12:29.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:12:31.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:12:33.208 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:12:33.208 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 12:12:33.208 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 12:12:33.208 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:12:33.235 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.97 ms, Average inference time: 7.28 ms

2025-08-28 12:12:33.236 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:12:33.320 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:12:33.407 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch150
2025-08-28 12:12:36.462 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 150/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.710e-03, size: 352, ETA: 2:34:38
2025-08-28 12:12:39.715 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 150/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.710e-03, size: 320, ETA: 2:34:35
2025-08-28 12:12:42.990 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 150/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.709e-03, size: 480, ETA: 2:34:32
2025-08-28 12:12:46.152 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 150/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.709e-03, size: 288, ETA: 2:34:29
2025-08-28 12:12:49.302 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 150/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.708e-03, size: 544, ETA: 2:34:25
2025-08-28 12:12:52.679 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 150/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.707e-03, size: 256, ETA: 2:34:23
2025-08-28 12:12:54.059 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:13:00.403 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:13:04.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:13:06.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5527
2025-08-28 12:13:07.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4658
2025-08-28 12:13:07.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3001
2025-08-28 12:13:07.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4395
2025-08-28 12:13:07.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:13:07.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:13:07.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.553
2025-08-28 12:13:07.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-08-28 12:13:07.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.300
2025-08-28 12:13:07.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.440
2025-08-28 12:13:07.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:13:07.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:13:07.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:13:07.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:13:07.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:13:07.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:13:07.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:13:07.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:13:07.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:13:10.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:13:13.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:13:16.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:13:19.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:13:22.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:13:25.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:13:29.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:13:32.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:13:35.208 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:13:35.209 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 12:13:35.209 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:13:35.209 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:13:35.237 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.97 ms, Average inference time: 7.22 ms

2025-08-28 12:13:35.238 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:13:35.315 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:13:35.395 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch151
2025-08-28 12:13:38.496 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 151/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.707e-03, size: 576, ETA: 2:34:17
2025-08-28 12:13:41.779 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 151/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.706e-03, size: 256, ETA: 2:34:14
2025-08-28 12:13:45.053 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 151/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.705e-03, size: 320, ETA: 2:34:11
2025-08-28 12:13:48.189 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 151/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.705e-03, size: 480, ETA: 2:34:08
2025-08-28 12:13:51.306 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 151/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.704e-03, size: 256, ETA: 2:34:04
2025-08-28 12:13:54.664 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 151/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.704e-03, size: 320, ETA: 2:34:01
2025-08-28 12:13:56.256 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:14:02.490 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:14:04.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:14:05.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5064
2025-08-28 12:14:05.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4331
2025-08-28 12:14:05.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2494
2025-08-28 12:14:05.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3963
2025-08-28 12:14:05.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:14:05.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:14:05.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-08-28 12:14:05.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.433
2025-08-28 12:14:05.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.249
2025-08-28 12:14:05.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.396
2025-08-28 12:14:05.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:14:05.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:14:05.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:14:05.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:14:05.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:14:05.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:14:05.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:14:05.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:14:05.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:14:06.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:14:08.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:14:09.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:14:10.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:14:12.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:14:13.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:14:14.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:14:16.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:14:17.713 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:14:17.714 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:14:17.714 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 12:14:17.714 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:14:17.721 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.92 ms, Average inference time: 7.06 ms

2025-08-28 12:14:17.724 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:14:17.799 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:14:17.884 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch152
2025-08-28 12:14:21.113 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 152/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.703e-03, size: 256, ETA: 2:33:57
2025-08-28 12:14:24.389 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 152/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.702e-03, size: 512, ETA: 2:33:54
2025-08-28 12:14:27.620 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 152/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.702e-03, size: 480, ETA: 2:33:51
2025-08-28 12:14:30.801 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 152/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.701e-03, size: 480, ETA: 2:33:48
2025-08-28 12:14:33.944 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 152/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.701e-03, size: 256, ETA: 2:33:44
2025-08-28 12:14:37.150 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 152/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 1.1, lr: 1.700e-03, size: 288, ETA: 2:33:41
2025-08-28 12:14:38.593 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:14:44.957 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:14:46.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:14:48.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5378
2025-08-28 12:14:48.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4905
2025-08-28 12:14:48.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2800
2025-08-28 12:14:48.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4361
2025-08-28 12:14:48.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:14:48.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:14:48.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.538
2025-08-28 12:14:48.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-28 12:14:48.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.280
2025-08-28 12:14:48.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.436
2025-08-28 12:14:48.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:14:48.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:14:48.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:14:48.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:14:48.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:14:48.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:14:48.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:14:48.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:14:48.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:14:50.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:14:51.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:14:53.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:14:55.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:14:56.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:14:58.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:15:00.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:15:01.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:15:03.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:15:03.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:15:03.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:15:03.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:15:03.511 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.02 ms, Average NMS time: 0.92 ms, Average inference time: 6.94 ms

2025-08-28 12:15:03.512 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:15:03.625 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:15:03.716 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch153
2025-08-28 12:15:06.832 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 153/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.699e-03, size: 544, ETA: 2:33:36
2025-08-28 12:15:10.148 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 153/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.699e-03, size: 416, ETA: 2:33:33
2025-08-28 12:15:13.414 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 153/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.698e-03, size: 256, ETA: 2:33:30
2025-08-28 12:15:16.728 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 153/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.697e-03, size: 448, ETA: 2:33:27
2025-08-28 12:15:20.025 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 153/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 1.697e-03, size: 448, ETA: 2:33:24
2025-08-28 12:15:23.293 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 153/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.696e-03, size: 384, ETA: 2:33:21
2025-08-28 12:15:24.752 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:15:31.152 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:15:34.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:15:36.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5406
2025-08-28 12:15:37.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4546
2025-08-28 12:15:37.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3371
2025-08-28 12:15:37.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4441
2025-08-28 12:15:37.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:15:37.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:15:37.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-08-28 12:15:37.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-08-28 12:15:37.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.337
2025-08-28 12:15:37.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.444
2025-08-28 12:15:37.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:15:37.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:15:37.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:15:37.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:15:37.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:15:37.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:15:37.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:15:37.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:15:37.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:15:40.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:15:43.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:15:45.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:15:48.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:15:51.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:15:54.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:15:57.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:15:59.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:16:02.727 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:16:02.728 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:16:02.728 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:16:02.728 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:16:02.754 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.99 ms, Average inference time: 7.08 ms

2025-08-28 12:16:02.755 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:16:02.843 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:16:02.931 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch154
2025-08-28 12:16:06.113 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 154/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.2, cls_loss: 0.9, lr: 1.695e-03, size: 352, ETA: 2:33:16
2025-08-28 12:16:09.531 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 154/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.3, lr: 1.695e-03, size: 416, ETA: 2:33:13
2025-08-28 12:16:12.857 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 154/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.694e-03, size: 448, ETA: 2:33:10
2025-08-28 12:16:16.184 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 154/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.694e-03, size: 448, ETA: 2:33:07
2025-08-28 12:16:19.481 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 154/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.693e-03, size: 576, ETA: 2:33:04
2025-08-28 12:16:22.821 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 154/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.692e-03, size: 384, ETA: 2:33:01
2025-08-28 12:16:24.259 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:16:30.490 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:16:33.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:16:36.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5421
2025-08-28 12:16:36.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4506
2025-08-28 12:16:36.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3186
2025-08-28 12:16:36.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4371
2025-08-28 12:16:36.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:16:36.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:16:36.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-08-28 12:16:36.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-28 12:16:36.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.319
2025-08-28 12:16:36.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.437
2025-08-28 12:16:36.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:16:36.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:16:36.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:16:36.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:16:36.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:16:36.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:16:36.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:16:36.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:16:36.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:16:39.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:16:41.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:16:44.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:16:47.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:16:50.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:16:52.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:16:55.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:16:57.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:17:00.585 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:17:00.586 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:17:00.586 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:17:00.586 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:17:00.615 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.97 ms, Average inference time: 7.06 ms

2025-08-28 12:17:00.616 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:17:00.739 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:17:00.821 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch155
2025-08-28 12:17:04.013 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 155/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.692e-03, size: 288, ETA: 2:32:57
2025-08-28 12:17:07.279 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 155/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.691e-03, size: 352, ETA: 2:32:54
2025-08-28 12:17:10.525 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 155/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.1, lr: 1.690e-03, size: 576, ETA: 2:32:50
2025-08-28 12:17:13.980 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 155/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.690e-03, size: 416, ETA: 2:32:48
2025-08-28 12:17:17.181 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 155/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.689e-03, size: 320, ETA: 2:32:44
2025-08-28 12:17:20.444 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 155/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.689e-03, size: 416, ETA: 2:32:41
2025-08-28 12:17:21.963 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:17:28.163 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:17:30.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:17:32.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5149
2025-08-28 12:17:32.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4583
2025-08-28 12:17:32.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3124
2025-08-28 12:17:32.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4286
2025-08-28 12:17:32.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:17:32.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:17:32.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 12:17:32.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.458
2025-08-28 12:17:32.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-08-28 12:17:32.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.429
2025-08-28 12:17:32.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:17:32.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:17:32.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:17:32.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:17:32.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:17:32.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:17:32.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:17:32.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:17:32.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:17:34.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:17:36.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:17:38.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:17:40.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:17:42.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:17:43.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:17:45.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:17:47.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:17:49.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:17:49.736 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:17:49.736 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:17:49.736 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:17:49.761 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.97 ms, Average inference time: 7.14 ms

2025-08-28 12:17:49.762 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:17:49.921 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:17:50.005 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch156
2025-08-28 12:17:53.140 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 156/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.688e-03, size: 320, ETA: 2:32:37
2025-08-28 12:17:56.418 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 156/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.687e-03, size: 448, ETA: 2:32:33
2025-08-28 12:17:59.699 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 156/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.687e-03, size: 256, ETA: 2:32:30
2025-08-28 12:18:02.805 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 156/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.686e-03, size: 288, ETA: 2:32:27
2025-08-28 12:18:05.926 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 156/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.685e-03, size: 384, ETA: 2:32:23
2025-08-28 12:18:09.167 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 156/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.685e-03, size: 480, ETA: 2:32:20
2025-08-28 12:18:10.586 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:18:17.130 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:18:20.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:18:22.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5577
2025-08-28 12:18:22.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4775
2025-08-28 12:18:22.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2969
2025-08-28 12:18:22.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4441
2025-08-28 12:18:22.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:18:22.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:18:22.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.558
2025-08-28 12:18:22.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.478
2025-08-28 12:18:22.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.297
2025-08-28 12:18:22.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.444
2025-08-28 12:18:22.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:18:22.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:18:22.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:18:22.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:18:22.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:18:22.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:18:22.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:18:22.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:18:22.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:18:25.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:18:27.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:18:30.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:18:32.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:18:35.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:18:37.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:18:39.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:18:42.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:18:44.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:18:44.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 12:18:44.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:18:44.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:18:44.888 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.08 ms, Average NMS time: 0.94 ms, Average inference time: 7.02 ms

2025-08-28 12:18:44.890 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:18:45.018 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:18:45.100 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch157
2025-08-28 12:18:48.142 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 157/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 1.0, lr: 1.684e-03, size: 320, ETA: 2:32:15
2025-08-28 12:18:51.402 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 157/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.683e-03, size: 384, ETA: 2:32:12
2025-08-28 12:18:54.786 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 157/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.683e-03, size: 384, ETA: 2:32:09
2025-08-28 12:18:58.224 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 157/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.170s, data_time: 0.001s, total_loss: 6.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.682e-03, size: 288, ETA: 2:32:06
2025-08-28 12:19:01.409 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 157/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.682e-03, size: 384, ETA: 2:32:03
2025-08-28 12:19:04.821 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 157/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.681e-03, size: 384, ETA: 2:32:00
2025-08-28 12:19:06.407 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:19:12.519 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:19:14.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:19:16.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5118
2025-08-28 12:19:16.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4724
2025-08-28 12:19:16.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2802
2025-08-28 12:19:16.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4215
2025-08-28 12:19:16.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:19:16.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:19:16.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-08-28 12:19:16.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-08-28 12:19:16.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.280
2025-08-28 12:19:16.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.421
2025-08-28 12:19:16.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:19:16.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:19:16.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:19:16.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:19:16.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:19:16.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:19:16.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:19:16.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:19:16.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:19:18.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:19:20.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:19:22.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:19:24.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:19:26.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:19:28.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:19:30.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:19:32.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:19:34.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:19:34.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:19:34.667 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 12:19:34.667 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:19:34.695 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.96 ms, Average inference time: 7.09 ms

2025-08-28 12:19:34.696 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:19:34.776 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:19:34.865 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch158
2025-08-28 12:19:37.912 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 158/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.680e-03, size: 480, ETA: 2:31:55
2025-08-28 12:19:41.287 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 158/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.166s, data_time: 0.005s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.680e-03, size: 288, ETA: 2:31:53
2025-08-28 12:19:44.417 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 158/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.679e-03, size: 256, ETA: 2:31:49
2025-08-28 12:19:47.520 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 158/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.678e-03, size: 320, ETA: 2:31:45
2025-08-28 12:19:50.716 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 158/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.7, lr: 1.678e-03, size: 544, ETA: 2:31:42
2025-08-28 12:19:53.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 158/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.677e-03, size: 288, ETA: 2:31:39
2025-08-28 12:19:55.440 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:20:01.790 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:20:04.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:20:06.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5318
2025-08-28 12:20:06.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4545
2025-08-28 12:20:06.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3453
2025-08-28 12:20:06.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4439
2025-08-28 12:20:06.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:20:06.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:20:06.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-08-28 12:20:06.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-08-28 12:20:06.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.345
2025-08-28 12:20:06.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.444
2025-08-28 12:20:06.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:20:06.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:20:06.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:20:06.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:20:06.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:20:06.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:20:06.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:20:06.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:20:06.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:20:08.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:20:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:20:13.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:20:15.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:20:17.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:20:19.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:20:22.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:20:24.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:20:26.347 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:20:26.348 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 12:20:26.348 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:20:26.348 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:20:26.373 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.96 ms, Average inference time: 7.09 ms

2025-08-28 12:20:26.374 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:20:26.460 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:20:26.542 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch159
2025-08-28 12:20:29.884 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 159/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.676e-03, size: 352, ETA: 2:31:35
2025-08-28 12:20:33.164 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 159/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.676e-03, size: 544, ETA: 2:31:32
2025-08-28 12:20:36.432 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 159/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.675e-03, size: 320, ETA: 2:31:29
2025-08-28 12:20:39.644 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 159/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.674e-03, size: 544, ETA: 2:31:25
2025-08-28 12:20:42.954 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 159/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.674e-03, size: 288, ETA: 2:31:22
2025-08-28 12:20:46.100 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 159/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.673e-03, size: 384, ETA: 2:31:19
2025-08-28 12:20:47.560 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:20:53.718 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:20:55.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:20:57.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5264
2025-08-28 12:20:57.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4604
2025-08-28 12:20:57.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3031
2025-08-28 12:20:57.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4300
2025-08-28 12:20:57.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:20:57.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:20:57.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-08-28 12:20:57.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-08-28 12:20:57.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-08-28 12:20:57.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.430
2025-08-28 12:20:57.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:20:57.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:20:57.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:20:57.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:20:57.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:20:57.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:20:57.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:20:57.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:20:57.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:20:59.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:21:01.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:21:03.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:21:04.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:21:06.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:21:08.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:21:10.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:21:11.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:21:13.787 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:21:13.787 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:21:13.787 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:21:13.787 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:21:13.812 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.98 ms, Average inference time: 7.28 ms

2025-08-28 12:21:13.814 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:21:13.902 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:21:14.032 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch160
2025-08-28 12:21:17.183 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 160/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.672e-03, size: 480, ETA: 2:31:14
2025-08-28 12:21:20.551 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 160/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.672e-03, size: 544, ETA: 2:31:11
2025-08-28 12:21:23.740 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 160/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.671e-03, size: 416, ETA: 2:31:08
2025-08-28 12:21:27.026 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 160/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.671e-03, size: 384, ETA: 2:31:05
2025-08-28 12:21:30.216 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 160/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.670e-03, size: 416, ETA: 2:31:01
2025-08-28 12:21:33.380 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 160/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.669e-03, size: 416, ETA: 2:30:58
2025-08-28 12:21:34.844 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:21:41.091 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:21:43.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:21:44.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5434
2025-08-28 12:21:45.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4718
2025-08-28 12:21:45.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3290
2025-08-28 12:21:45.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4481
2025-08-28 12:21:45.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:21:45.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:21:45.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-08-28 12:21:45.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-08-28 12:21:45.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.329
2025-08-28 12:21:45.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.448
2025-08-28 12:21:45.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:21:45.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:21:45.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:21:45.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:21:45.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:21:45.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:21:45.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:21:45.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:21:45.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:21:47.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:21:49.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:21:51.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:21:53.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:21:55.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:21:57.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:21:59.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:22:00.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:22:02.838 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:22:02.838 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 12:22:02.838 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 12:22:02.838 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:22:02.867 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.94 ms, Average inference time: 7.06 ms

2025-08-28 12:22:02.868 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:22:02.946 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:22:03.076 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch161
2025-08-28 12:22:06.131 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 161/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.669e-03, size: 320, ETA: 2:30:53
2025-08-28 12:22:09.625 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 161/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.172s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.668e-03, size: 448, ETA: 2:30:50
2025-08-28 12:22:12.784 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 161/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.1, lr: 1.667e-03, size: 512, ETA: 2:30:47
2025-08-28 12:22:15.971 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 161/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.667e-03, size: 416, ETA: 2:30:44
2025-08-28 12:22:19.188 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 161/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.666e-03, size: 288, ETA: 2:30:41
2025-08-28 12:22:22.633 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 161/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.666e-03, size: 576, ETA: 2:30:38
2025-08-28 12:22:24.229 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:22:30.493 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:22:32.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:22:33.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4979
2025-08-28 12:22:34.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4568
2025-08-28 12:22:34.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3182
2025-08-28 12:22:34.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4243
2025-08-28 12:22:34.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:22:34.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:22:34.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-28 12:22:34.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-28 12:22:34.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-08-28 12:22:34.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.424
2025-08-28 12:22:34.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:22:34.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:22:34.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:22:34.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:22:34.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:22:34.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:22:34.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:22:34.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:22:34.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:22:36.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:22:37.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:22:39.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:22:40.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:22:42.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:22:44.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:22:45.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:22:47.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:22:49.213 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:22:49.214 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 12:22:49.214 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 12:22:49.214 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:22:49.243 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.95 ms, Average inference time: 7.07 ms

2025-08-28 12:22:49.244 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:22:49.352 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:22:49.458 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch162
2025-08-28 12:22:52.524 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 162/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.665e-03, size: 352, ETA: 2:30:33
2025-08-28 12:22:55.798 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 162/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.664e-03, size: 288, ETA: 2:30:30
2025-08-28 12:22:59.034 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 162/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 1.0, lr: 1.663e-03, size: 384, ETA: 2:30:27
2025-08-28 12:23:02.290 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 162/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.663e-03, size: 480, ETA: 2:30:24
2025-08-28 12:23:05.637 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 162/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.662e-03, size: 576, ETA: 2:30:21
2025-08-28 12:23:08.852 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 162/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.2, lr: 1.662e-03, size: 256, ETA: 2:30:18
2025-08-28 12:23:10.306 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:23:16.712 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:23:20.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:23:22.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5453
2025-08-28 12:23:22.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4455
2025-08-28 12:23:22.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3109
2025-08-28 12:23:22.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4339
2025-08-28 12:23:22.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:23:22.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:23:22.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-08-28 12:23:22.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.446
2025-08-28 12:23:22.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.311
2025-08-28 12:23:22.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-08-28 12:23:22.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:23:22.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:23:22.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:23:22.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:23:22.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:23:22.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:23:22.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:23:22.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:23:22.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:23:25.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:23:28.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:23:31.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:23:34.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:23:36.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:23:39.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:23:42.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:23:45.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:23:47.946 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:23:47.946 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:23:47.946 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:23:47.946 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:23:47.973 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.96 ms, Average inference time: 7.19 ms

2025-08-28 12:23:47.974 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:23:48.110 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:23:48.193 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch163
2025-08-28 12:23:51.317 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 163/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.661e-03, size: 256, ETA: 2:30:13
2025-08-28 12:23:54.553 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 163/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.2, lr: 1.660e-03, size: 448, ETA: 2:30:09
2025-08-28 12:23:57.850 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 163/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.659e-03, size: 480, ETA: 2:30:06
2025-08-28 12:24:01.088 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 163/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 1.0, lr: 1.659e-03, size: 544, ETA: 2:30:03
2025-08-28 12:24:04.377 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 163/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.658e-03, size: 416, ETA: 2:30:00
2025-08-28 12:24:07.698 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 163/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.658e-03, size: 512, ETA: 2:29:57
2025-08-28 12:24:09.179 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:24:15.448 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:24:17.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:24:18.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5407
2025-08-28 12:24:18.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4664
2025-08-28 12:24:18.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3103
2025-08-28 12:24:18.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4391
2025-08-28 12:24:18.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:24:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:24:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-08-28 12:24:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-08-28 12:24:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.310
2025-08-28 12:24:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.439
2025-08-28 12:24:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:24:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:24:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:24:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:24:18.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:24:18.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:24:18.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:24:18.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:24:18.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:24:20.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:24:21.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:24:22.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:24:24.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:24:25.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:24:26.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:24:28.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:24:29.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:24:31.032 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:24:31.032 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 12:24:31.032 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:24:31.032 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:24:31.042 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.97 ms, Average inference time: 7.14 ms

2025-08-28 12:24:31.043 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:24:31.117 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:24:31.205 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch164
2025-08-28 12:24:34.439 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 164/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.657e-03, size: 320, ETA: 2:29:53
2025-08-28 12:24:37.699 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 164/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.656e-03, size: 384, ETA: 2:29:50
2025-08-28 12:24:41.079 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 164/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.656e-03, size: 576, ETA: 2:29:47
2025-08-28 12:24:44.509 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 164/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.655e-03, size: 352, ETA: 2:29:44
2025-08-28 12:24:47.832 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 164/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.9, lr: 1.654e-03, size: 576, ETA: 2:29:41
2025-08-28 12:24:51.292 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 164/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.654e-03, size: 576, ETA: 2:29:38
2025-08-28 12:24:52.849 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:24:59.025 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:25:01.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:25:03.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5585
2025-08-28 12:25:03.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4825
2025-08-28 12:25:04.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3180
2025-08-28 12:25:04.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4530
2025-08-28 12:25:04.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:25:04.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:25:04.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.558
2025-08-28 12:25:04.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-28 12:25:04.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-08-28 12:25:04.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.453
2025-08-28 12:25:04.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:25:04.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:25:04.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:25:04.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:25:04.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:25:04.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:25:04.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:25:04.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:25:04.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:25:06.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:25:08.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:25:10.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:25:13.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:25:15.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:25:17.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:25:19.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:25:22.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:25:24.510 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:25:24.511 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 12:25:24.511 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 12:25:24.511 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:25:24.538 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.99 ms, Average inference time: 7.09 ms

2025-08-28 12:25:24.539 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:25:24.623 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:25:24.704 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch165
2025-08-28 12:25:27.870 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 165/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.653e-03, size: 320, ETA: 2:29:34
2025-08-28 12:25:30.983 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 165/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.652e-03, size: 416, ETA: 2:29:30
2025-08-28 12:25:34.257 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 165/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.652e-03, size: 384, ETA: 2:29:27
2025-08-28 12:25:37.390 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 165/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.651e-03, size: 288, ETA: 2:29:24
2025-08-28 12:25:40.764 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 165/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.650e-03, size: 288, ETA: 2:29:21
2025-08-28 12:25:44.024 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 165/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.650e-03, size: 352, ETA: 2:29:18
2025-08-28 12:25:45.407 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:25:51.763 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:25:54.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:25:55.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5354
2025-08-28 12:25:55.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4684
2025-08-28 12:25:55.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3335
2025-08-28 12:25:55.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4457
2025-08-28 12:25:55.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:25:55.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:25:55.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-08-28 12:25:55.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-08-28 12:25:55.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-08-28 12:25:55.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.446
2025-08-28 12:25:55.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:25:55.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:25:55.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:25:55.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:25:55.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:25:55.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:25:55.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:25:55.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:25:55.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:25:57.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:25:59.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:26:01.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:26:03.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:26:05.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:26:07.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:26:09.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:26:10.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:26:12.826 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:26:12.827 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:26:12.827 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 12:26:12.827 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:26:12.853 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.97 ms, Average inference time: 7.24 ms

2025-08-28 12:26:12.854 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:26:12.944 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:26:13.085 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch166
2025-08-28 12:26:16.146 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 166/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.649e-03, size: 320, ETA: 2:29:13
2025-08-28 12:26:19.492 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 166/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.648e-03, size: 512, ETA: 2:29:10
2025-08-28 12:26:22.730 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 166/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.648e-03, size: 576, ETA: 2:29:06
2025-08-28 12:26:26.227 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 166/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.172s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.647e-03, size: 352, ETA: 2:29:04
2025-08-28 12:26:29.439 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 166/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.646e-03, size: 256, ETA: 2:29:01
2025-08-28 12:26:32.609 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 166/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.646e-03, size: 384, ETA: 2:28:57
2025-08-28 12:26:34.084 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:26:40.322 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:26:42.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:26:44.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5591
2025-08-28 12:26:44.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4790
2025-08-28 12:26:45.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3113
2025-08-28 12:26:45.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4498
2025-08-28 12:26:45.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:26:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:26:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-08-28 12:26:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-08-28 12:26:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.311
2025-08-28 12:26:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.450
2025-08-28 12:26:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:26:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:26:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:26:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:26:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:26:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:26:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:26:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:26:45.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:26:47.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:26:49.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:26:51.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:26:53.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:26:55.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:26:57.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:26:59.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:27:01.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:27:03.751 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:27:03.752 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 12:27:03.752 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 12:27:03.752 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:27:03.777 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 1.00 ms, Average inference time: 7.22 ms

2025-08-28 12:27:03.778 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:27:03.856 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:27:03.986 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch167
2025-08-28 12:27:07.006 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 167/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.645e-03, size: 384, ETA: 2:28:52
2025-08-28 12:27:10.316 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 167/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.644e-03, size: 480, ETA: 2:28:49
2025-08-28 12:27:13.645 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 167/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.644e-03, size: 288, ETA: 2:28:46
2025-08-28 12:27:16.682 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 167/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.643e-03, size: 256, ETA: 2:28:42
2025-08-28 12:27:20.096 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 167/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.642e-03, size: 288, ETA: 2:28:40
2025-08-28 12:27:23.466 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 167/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.642e-03, size: 480, ETA: 2:28:37
2025-08-28 12:27:24.902 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:27:31.127 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:27:33.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:27:34.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5362
2025-08-28 12:27:34.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4817
2025-08-28 12:27:34.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3065
2025-08-28 12:27:34.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4415
2025-08-28 12:27:34.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:27:34.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:27:34.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-08-28 12:27:34.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-28 12:27:34.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.306
2025-08-28 12:27:34.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.441
2025-08-28 12:27:34.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:27:34.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:27:34.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:27:34.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:27:34.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:27:34.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:27:34.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:27:34.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:27:34.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:27:36.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:27:38.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:27:40.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:27:41.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:27:43.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:27:45.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:27:46.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:27:48.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:27:50.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:27:50.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:27:50.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:27:50.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:27:50.096 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.94 ms, Average inference time: 7.16 ms

2025-08-28 12:27:50.098 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:27:50.178 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:27:50.266 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch168
2025-08-28 12:27:53.399 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 168/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.641e-03, size: 256, ETA: 2:28:32
2025-08-28 12:27:56.575 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 168/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.640e-03, size: 512, ETA: 2:28:28
2025-08-28 12:27:59.742 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 168/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.640e-03, size: 352, ETA: 2:28:25
2025-08-28 12:28:02.929 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 168/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.639e-03, size: 448, ETA: 2:28:22
2025-08-28 12:28:06.190 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 168/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.638e-03, size: 480, ETA: 2:28:19
2025-08-28 12:28:09.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 168/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.638e-03, size: 416, ETA: 2:28:15
2025-08-28 12:28:10.911 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:28:17.264 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:28:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:28:20.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5473
2025-08-28 12:28:21.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4622
2025-08-28 12:28:21.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3090
2025-08-28 12:28:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4395
2025-08-28 12:28:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:28:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:28:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.547
2025-08-28 12:28:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-08-28 12:28:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.309
2025-08-28 12:28:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.439
2025-08-28 12:28:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:28:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:28:21.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:28:21.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:28:21.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:28:21.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:28:21.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:28:21.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:28:21.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:28:22.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:28:24.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:28:26.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:28:28.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:28:29.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:28:31.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:28:33.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:28:34.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:28:36.647 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:28:36.648 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:28:36.648 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:28:36.648 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:28:36.674 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.97 ms, Average inference time: 7.20 ms

2025-08-28 12:28:36.675 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:28:36.770 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:28:36.855 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch169
2025-08-28 12:28:40.017 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 169/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.637e-03, size: 288, ETA: 2:28:11
2025-08-28 12:28:43.220 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 169/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.005s, total_loss: 7.2, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.636e-03, size: 448, ETA: 2:28:07
2025-08-28 12:28:46.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 169/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.636e-03, size: 416, ETA: 2:28:04
2025-08-28 12:28:49.693 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 169/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.635e-03, size: 352, ETA: 2:28:01
2025-08-28 12:28:52.804 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 169/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.634e-03, size: 320, ETA: 2:27:57
2025-08-28 12:28:56.091 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 169/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.634e-03, size: 576, ETA: 2:27:54
2025-08-28 12:28:57.596 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:29:03.727 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:29:06.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:29:07.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5298
2025-08-28 12:29:08.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4588
2025-08-28 12:29:08.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2929
2025-08-28 12:29:08.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4272
2025-08-28 12:29:08.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:29:08.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:29:08.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 12:29:08.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.459
2025-08-28 12:29:08.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.293
2025-08-28 12:29:08.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.427
2025-08-28 12:29:08.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:29:08.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:29:08.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:29:08.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:29:08.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:29:08.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:29:08.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:29:08.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:29:08.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:29:10.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:29:12.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:29:14.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:29:16.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:29:18.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:29:20.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:29:22.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:29:24.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:29:26.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:29:26.641 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:29:26.641 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:29:26.641 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:29:26.667 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.96 ms, Average inference time: 7.14 ms

2025-08-28 12:29:26.668 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:29:26.752 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:29:26.839 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch170
2025-08-28 12:29:29.956 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 170/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.633e-03, size: 544, ETA: 2:27:50
2025-08-28 12:29:33.345 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 170/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.167s, data_time: 0.003s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.2, lr: 1.632e-03, size: 288, ETA: 2:27:47
2025-08-28 12:29:36.643 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 170/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 1.0, lr: 1.631e-03, size: 544, ETA: 2:27:44
2025-08-28 12:29:39.992 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 170/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.631e-03, size: 512, ETA: 2:27:41
2025-08-28 12:29:43.223 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 170/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.2, lr: 1.630e-03, size: 352, ETA: 2:27:38
2025-08-28 12:29:46.388 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 170/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 1.630e-03, size: 512, ETA: 2:27:34
2025-08-28 12:29:47.802 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:29:54.045 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:29:56.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:29:57.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5660
2025-08-28 12:29:58.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4922
2025-08-28 12:29:58.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3317
2025-08-28 12:29:58.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4633
2025-08-28 12:29:58.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:29:58.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:29:58.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.566
2025-08-28 12:29:58.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.492
2025-08-28 12:29:58.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.332
2025-08-28 12:29:58.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.463
2025-08-28 12:29:58.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:29:58.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:29:58.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:29:58.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:29:58.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:29:58.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:29:58.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:29:58.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:29:58.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:30:00.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:30:01.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:30:03.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:30:05.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:30:07.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:30:09.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:30:10.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:30:12.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:30:14.543 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:30:14.544 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 12:30:14.544 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 12:30:14.544 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:30:14.570 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.94 ms, Average inference time: 7.17 ms

2025-08-28 12:30:14.571 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:30:14.649 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:30:14.733 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch171
2025-08-28 12:30:17.723 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 171/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.629e-03, size: 384, ETA: 2:27:29
2025-08-28 12:30:20.976 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 171/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.628e-03, size: 544, ETA: 2:27:26
2025-08-28 12:30:24.182 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 171/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.627e-03, size: 320, ETA: 2:27:22
2025-08-28 12:30:27.466 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 171/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.627e-03, size: 576, ETA: 2:27:19
2025-08-28 12:30:30.744 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 171/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.626e-03, size: 384, ETA: 2:27:16
2025-08-28 12:30:34.135 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 171/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.626e-03, size: 576, ETA: 2:27:13
2025-08-28 12:30:35.623 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:30:41.819 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:30:43.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:30:44.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5140
2025-08-28 12:30:45.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4587
2025-08-28 12:30:45.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2901
2025-08-28 12:30:45.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4209
2025-08-28 12:30:45.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:30:45.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:30:45.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.514
2025-08-28 12:30:45.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.459
2025-08-28 12:30:45.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-08-28 12:30:45.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.421
2025-08-28 12:30:45.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:30:45.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:30:45.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:30:45.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:30:45.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:30:45.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:30:45.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:30:45.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:30:45.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:30:46.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:30:48.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:30:49.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:30:51.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:30:52.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:30:54.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:30:55.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:30:57.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:30:58.570 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:30:58.570 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:30:58.570 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 12:30:58.570 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:30:58.580 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.98 ms, Average inference time: 7.18 ms

2025-08-28 12:30:58.581 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:30:58.656 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:30:58.782 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch172
2025-08-28 12:31:01.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 172/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.625e-03, size: 576, ETA: 2:27:09
2025-08-28 12:31:05.462 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 172/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.174s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.624e-03, size: 448, ETA: 2:27:06
2025-08-28 12:31:08.668 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 172/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.623e-03, size: 512, ETA: 2:27:03
2025-08-28 12:31:11.911 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 172/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.623e-03, size: 384, ETA: 2:27:00
2025-08-28 12:31:15.103 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 172/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.622e-03, size: 256, ETA: 2:26:56
2025-08-28 12:31:18.292 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 172/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.621e-03, size: 480, ETA: 2:26:53
2025-08-28 12:31:19.797 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:31:26.011 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:31:27.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:31:28.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5567
2025-08-28 12:31:28.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4895
2025-08-28 12:31:29.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3205
2025-08-28 12:31:29.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4555
2025-08-28 12:31:29.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:31:29.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:31:29.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.557
2025-08-28 12:31:29.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-08-28 12:31:29.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.320
2025-08-28 12:31:29.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.456
2025-08-28 12:31:29.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:31:29.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:31:29.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:31:29.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:31:29.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:31:29.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:31:29.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:31:29.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:31:29.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:31:30.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:31:31.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:31:33.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:31:34.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:31:35.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:31:37.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:31:38.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:31:39.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:31:41.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:31:41.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 12:31:41.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 12:31:41.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:31:41.121 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.94 ms, Average inference time: 7.17 ms

2025-08-28 12:31:41.122 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:31:41.200 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:31:41.333 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch173
2025-08-28 12:31:44.454 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 173/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.621e-03, size: 416, ETA: 2:26:48
2025-08-28 12:31:47.608 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 173/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.620e-03, size: 256, ETA: 2:26:45
2025-08-28 12:31:50.793 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 173/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.619e-03, size: 256, ETA: 2:26:41
2025-08-28 12:31:53.981 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 173/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.619e-03, size: 544, ETA: 2:26:38
2025-08-28 12:31:57.211 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 173/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.618e-03, size: 384, ETA: 2:26:35
2025-08-28 12:32:00.353 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 173/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.617e-03, size: 384, ETA: 2:26:32
2025-08-28 12:32:01.792 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:32:08.048 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:32:11.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:32:14.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5068
2025-08-28 12:32:15.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4332
2025-08-28 12:32:15.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3110
2025-08-28 12:32:15.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4170
2025-08-28 12:32:15.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:32:15.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:32:15.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-08-28 12:32:15.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.433
2025-08-28 12:32:15.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.311
2025-08-28 12:32:15.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.417
2025-08-28 12:32:15.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:32:15.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:32:15.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:32:15.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:32:15.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:32:15.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:32:15.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:32:15.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:32:15.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:32:18.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:32:21.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:32:24.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:32:27.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:32:31.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:32:34.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:32:37.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:32:40.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:32:43.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:32:43.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 12:32:43.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 12:32:43.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:32:43.788 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.07 ms, Average NMS time: 0.96 ms, Average inference time: 7.03 ms

2025-08-28 12:32:43.789 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:32:43.867 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:32:43.968 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch174
2025-08-28 12:32:47.168 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 174/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.616e-03, size: 320, ETA: 2:26:27
2025-08-28 12:32:50.317 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 174/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.616e-03, size: 256, ETA: 2:26:23
2025-08-28 12:32:53.524 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 174/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.615e-03, size: 480, ETA: 2:26:20
2025-08-28 12:32:56.764 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 174/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.614e-03, size: 320, ETA: 2:26:17
2025-08-28 12:33:00.017 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 174/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.614e-03, size: 576, ETA: 2:26:14
2025-08-28 12:33:03.498 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 174/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.613e-03, size: 576, ETA: 2:26:11
2025-08-28 12:33:05.022 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:33:11.314 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:33:13.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:33:14.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5617
2025-08-28 12:33:14.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4739
2025-08-28 12:33:14.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3117
2025-08-28 12:33:14.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4491
2025-08-28 12:33:14.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:33:14.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:33:14.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.562
2025-08-28 12:33:14.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-28 12:33:14.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-08-28 12:33:14.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.449
2025-08-28 12:33:14.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:33:14.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:33:14.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:33:14.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:33:14.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:33:14.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:33:14.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:33:14.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:33:14.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:33:16.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:33:18.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:33:19.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:33:21.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:33:22.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:33:24.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:33:25.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:33:27.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:33:29.160 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:33:29.160 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 12:33:29.160 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 12:33:29.160 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:33:29.184 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.99 ms, Average inference time: 7.31 ms

2025-08-28 12:33:29.186 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:33:29.266 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:33:29.350 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch175
2025-08-28 12:33:32.383 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 175/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.612e-03, size: 384, ETA: 2:26:06
2025-08-28 12:33:35.836 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 175/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.170s, data_time: 0.003s, total_loss: 5.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 1.8, cls_loss: 0.7, lr: 1.612e-03, size: 448, ETA: 2:26:04
2025-08-28 12:33:39.110 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 175/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.611e-03, size: 480, ETA: 2:26:00
2025-08-28 12:33:42.253 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 175/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.610e-03, size: 256, ETA: 2:25:57
2025-08-28 12:33:45.649 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 175/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 8.8, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 4.3, cls_loss: 1.1, lr: 1.610e-03, size: 512, ETA: 2:25:54
2025-08-28 12:33:48.995 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 175/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 1.1, lr: 1.609e-03, size: 384, ETA: 2:25:51
2025-08-28 12:33:50.471 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:33:56.698 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:34:01.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:34:04.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5116
2025-08-28 12:34:05.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3874
2025-08-28 12:34:05.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2720
2025-08-28 12:34:05.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3903
2025-08-28 12:34:05.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:34:05.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:34:05.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-08-28 12:34:05.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-28 12:34:05.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.272
2025-08-28 12:34:05.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.390
2025-08-28 12:34:05.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:34:05.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:34:05.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:34:05.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:34:05.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:34:05.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:34:05.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:34:05.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:34:05.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:34:08.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:34:12.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:34:16.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:34:20.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:34:23.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:34:27.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:34:31.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:34:35.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:34:38.854 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:34:38.854 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-28 12:34:38.854 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-28 12:34:38.854 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:34:38.880 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.98 ms, Average inference time: 7.20 ms

2025-08-28 12:34:38.881 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:34:38.958 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:34:39.044 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch176
2025-08-28 12:34:42.251 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 176/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.157s, data_time: 0.005s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.608e-03, size: 576, ETA: 2:25:46
2025-08-28 12:34:45.541 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 176/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.607e-03, size: 288, ETA: 2:25:43
2025-08-28 12:34:48.723 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 176/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.607e-03, size: 416, ETA: 2:25:40
2025-08-28 12:34:51.896 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 176/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.606e-03, size: 448, ETA: 2:25:37
2025-08-28 12:34:55.206 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 176/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.606e-03, size: 576, ETA: 2:25:34
2025-08-28 12:34:58.445 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 176/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.605e-03, size: 512, ETA: 2:25:31
2025-08-28 12:34:59.970 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:35:06.148 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:35:08.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:35:09.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5387
2025-08-28 12:35:10.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4548
2025-08-28 12:35:10.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2747
2025-08-28 12:35:10.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4227
2025-08-28 12:35:10.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:35:10.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:35:10.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-08-28 12:35:10.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-08-28 12:35:10.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-08-28 12:35:10.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.423
2025-08-28 12:35:10.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:35:10.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:35:10.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:35:10.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:35:10.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:35:10.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:35:10.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:35:10.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:35:10.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:35:12.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:35:14.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:35:15.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:35:17.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:35:19.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:35:21.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:35:23.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:35:24.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:35:26.754 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:35:26.754 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:35:26.754 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 12:35:26.755 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:35:26.787 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.97 ms, Average inference time: 7.18 ms

2025-08-28 12:35:26.788 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:35:26.960 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:35:27.054 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch177
2025-08-28 12:35:30.299 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 177/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.604e-03, size: 352, ETA: 2:25:26
2025-08-28 12:35:33.428 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 177/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.603e-03, size: 480, ETA: 2:25:23
2025-08-28 12:35:36.777 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 177/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.603e-03, size: 384, ETA: 2:25:20
2025-08-28 12:35:40.019 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 177/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.9, lr: 1.602e-03, size: 384, ETA: 2:25:16
2025-08-28 12:35:43.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 177/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.601e-03, size: 448, ETA: 2:25:13
2025-08-28 12:35:46.528 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 177/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.601e-03, size: 544, ETA: 2:25:10
2025-08-28 12:35:47.991 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:35:54.256 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:35:58.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:36:01.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5301
2025-08-28 12:36:02.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4770
2025-08-28 12:36:02.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2977
2025-08-28 12:36:02.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4350
2025-08-28 12:36:02.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:36:02.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:36:02.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 12:36:02.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.477
2025-08-28 12:36:02.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.298
2025-08-28 12:36:02.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.435
2025-08-28 12:36:02.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:36:02.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:36:02.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:36:02.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:36:02.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:36:02.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:36:02.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:36:02.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:36:02.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:36:06.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:36:09.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:36:13.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:36:17.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:36:20.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:36:24.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:36:28.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:36:31.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:36:35.357 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:36:35.357 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 12:36:35.358 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:36:35.358 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:36:35.384 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 1.01 ms, Average inference time: 7.15 ms

2025-08-28 12:36:35.386 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:36:35.472 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:36:35.559 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch178
2025-08-28 12:36:38.688 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 178/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.005s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.600e-03, size: 352, ETA: 2:25:05
2025-08-28 12:36:41.946 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 178/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.3, cls_loss: 0.9, lr: 1.599e-03, size: 576, ETA: 2:25:02
2025-08-28 12:36:45.211 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 178/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.598e-03, size: 480, ETA: 2:24:59
2025-08-28 12:36:48.426 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 178/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.598e-03, size: 480, ETA: 2:24:56
2025-08-28 12:36:51.628 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 178/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.597e-03, size: 512, ETA: 2:24:53
2025-08-28 12:36:54.971 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 178/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.597e-03, size: 384, ETA: 2:24:50
2025-08-28 12:36:56.431 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:37:02.777 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:37:05.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:37:06.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5513
2025-08-28 12:37:06.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5182
2025-08-28 12:37:06.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3073
2025-08-28 12:37:06.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4589
2025-08-28 12:37:06.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:37:06.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:37:06.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-08-28 12:37:06.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.518
2025-08-28 12:37:06.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.307
2025-08-28 12:37:06.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.459
2025-08-28 12:37:06.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:37:06.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:37:06.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:37:06.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:37:06.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:37:06.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:37:06.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:37:06.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:37:06.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:37:08.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:37:10.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:37:12.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:37:14.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:37:16.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:37:18.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:37:19.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:37:21.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:37:23.554 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:37:23.554 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 12:37:23.554 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 12:37:23.554 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:37:23.580 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.98 ms, Average inference time: 7.12 ms

2025-08-28 12:37:23.581 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:37:23.663 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:37:23.746 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch179
2025-08-28 12:37:27.077 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 179/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.596e-03, size: 256, ETA: 2:24:45
2025-08-28 12:37:30.212 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 179/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.595e-03, size: 384, ETA: 2:24:42
2025-08-28 12:37:33.487 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 179/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.594e-03, size: 544, ETA: 2:24:39
2025-08-28 12:37:36.844 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 179/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.594e-03, size: 576, ETA: 2:24:36
2025-08-28 12:37:40.184 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 179/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 1.0, lr: 1.593e-03, size: 544, ETA: 2:24:33
2025-08-28 12:37:43.401 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 179/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.592e-03, size: 384, ETA: 2:24:30
2025-08-28 12:37:44.787 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:37:51.035 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:37:53.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:37:54.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5515
2025-08-28 12:37:54.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5005
2025-08-28 12:37:54.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2961
2025-08-28 12:37:54.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4494
2025-08-28 12:37:54.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:37:54.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:37:54.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.552
2025-08-28 12:37:54.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-08-28 12:37:54.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.296
2025-08-28 12:37:54.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.449
2025-08-28 12:37:54.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:37:54.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:37:54.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:37:54.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:37:54.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:37:54.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:37:54.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:37:54.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:37:54.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:37:56.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:37:57.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:37:59.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:38:00.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:38:02.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:38:03.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:38:05.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:38:07.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:38:08.563 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:38:08.563 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 12:38:08.563 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 12:38:08.564 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:38:08.592 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.96 ms, Average inference time: 7.11 ms

2025-08-28 12:38:08.593 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:38:08.679 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:38:08.765 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch180
2025-08-28 12:38:12.002 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 180/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.591e-03, size: 384, ETA: 2:24:25
2025-08-28 12:38:15.287 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 180/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.591e-03, size: 480, ETA: 2:24:22
2025-08-28 12:38:18.463 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 180/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.590e-03, size: 480, ETA: 2:24:18
2025-08-28 12:38:21.732 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 180/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.589e-03, size: 256, ETA: 2:24:15
2025-08-28 12:38:24.830 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 180/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 1.589e-03, size: 416, ETA: 2:24:12
2025-08-28 12:38:28.041 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 180/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.588e-03, size: 448, ETA: 2:24:08
2025-08-28 12:38:29.558 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:38:35.844 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:38:38.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:38:39.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5287
2025-08-28 12:38:40.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4972
2025-08-28 12:38:40.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3156
2025-08-28 12:38:40.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4472
2025-08-28 12:38:40.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:38:40.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:38:40.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-08-28 12:38:40.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-08-28 12:38:40.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-28 12:38:40.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.447
2025-08-28 12:38:40.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:38:40.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:38:40.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:38:40.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:38:40.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:38:40.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:38:40.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:38:40.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:38:40.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:38:42.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:38:44.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:38:46.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:38:48.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:38:50.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:38:52.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:38:54.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:38:55.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:38:57.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:38:57.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 12:38:57.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 12:38:57.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:38:57.945 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.98 ms, Average inference time: 7.24 ms

2025-08-28 12:38:57.946 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:38:58.025 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:38:58.110 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch181
2025-08-28 12:39:01.260 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 181/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.587e-03, size: 576, ETA: 2:24:04
2025-08-28 12:39:04.550 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 181/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.586e-03, size: 512, ETA: 2:24:01
2025-08-28 12:39:07.812 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 181/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.586e-03, size: 384, ETA: 2:23:57
2025-08-28 12:39:11.020 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 181/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.585e-03, size: 352, ETA: 2:23:54
2025-08-28 12:39:14.129 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 181/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.584e-03, size: 256, ETA: 2:23:51
2025-08-28 12:39:17.440 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 181/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.584e-03, size: 320, ETA: 2:23:48
2025-08-28 12:39:18.856 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:39:25.149 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:39:27.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:39:29.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5385
2025-08-28 12:39:30.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4488
2025-08-28 12:39:30.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3031
2025-08-28 12:39:30.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4301
2025-08-28 12:39:30.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:39:30.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:39:30.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-08-28 12:39:30.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-08-28 12:39:30.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-08-28 12:39:30.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.430
2025-08-28 12:39:30.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:39:30.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:39:30.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:39:30.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:39:30.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:39:30.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:39:30.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:39:30.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:39:30.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:39:32.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:39:34.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:39:37.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:39:39.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:39:41.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:39:44.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:39:46.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:39:48.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:39:51.132 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:39:51.132 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:39:51.132 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:39:51.133 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:39:51.165 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.98 ms, Average inference time: 7.17 ms

2025-08-28 12:39:51.166 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:39:51.248 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:39:51.337 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch182
2025-08-28 12:39:54.499 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 182/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 7.4, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.583e-03, size: 288, ETA: 2:23:43
2025-08-28 12:39:57.627 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 182/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 1.7, cls_loss: 0.7, lr: 1.582e-03, size: 512, ETA: 2:23:39
2025-08-28 12:40:00.898 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 182/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.582e-03, size: 416, ETA: 2:23:36
2025-08-28 12:40:04.236 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 182/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.581e-03, size: 256, ETA: 2:23:33
2025-08-28 12:40:07.319 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 182/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.580e-03, size: 352, ETA: 2:23:30
2025-08-28 12:40:10.510 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 182/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.580e-03, size: 448, ETA: 2:23:26
2025-08-28 12:40:11.938 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:40:18.296 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:40:20.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:40:22.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5457
2025-08-28 12:40:22.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4663
2025-08-28 12:40:22.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2710
2025-08-28 12:40:22.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4277
2025-08-28 12:40:22.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:40:22.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:40:22.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.546
2025-08-28 12:40:22.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-08-28 12:40:22.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.271
2025-08-28 12:40:22.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.428
2025-08-28 12:40:22.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:40:22.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:40:22.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:40:22.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:40:22.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:40:22.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:40:22.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:40:22.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:40:22.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:40:24.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:40:26.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:40:29.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:40:31.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:40:33.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:40:35.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:40:37.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:40:39.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:40:41.205 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:40:41.205 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:40:41.205 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:40:41.205 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:40:41.232 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.36 ms, Average NMS time: 0.98 ms, Average inference time: 7.34 ms

2025-08-28 12:40:41.234 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:40:41.315 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:40:41.446 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch183
2025-08-28 12:40:44.555 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 183/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.579e-03, size: 352, ETA: 2:23:21
2025-08-28 12:40:47.760 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 183/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.578e-03, size: 384, ETA: 2:23:18
2025-08-28 12:40:50.949 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 183/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.577e-03, size: 544, ETA: 2:23:15
2025-08-28 12:40:54.257 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 183/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.7Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.577e-03, size: 576, ETA: 2:23:12
2025-08-28 12:40:57.672 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 183/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.576e-03, size: 320, ETA: 2:23:09
2025-08-28 12:41:00.953 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 183/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.575e-03, size: 544, ETA: 2:23:06
2025-08-28 12:41:02.484 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:41:08.650 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:41:10.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:41:11.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4477
2025-08-28 12:41:11.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4532
2025-08-28 12:41:11.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2644
2025-08-28 12:41:11.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3885
2025-08-28 12:41:11.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:41:11.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:41:11.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.448
2025-08-28 12:41:11.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.453
2025-08-28 12:41:11.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.264
2025-08-28 12:41:11.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.388
2025-08-28 12:41:11.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:41:11.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:41:11.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:41:11.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:41:11.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:41:11.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:41:11.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:41:11.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:41:11.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:41:12.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:41:13.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:41:15.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:41:16.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:41:17.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:41:18.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:41:19.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:41:20.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:41:22.097 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:41:22.097 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-08-28 12:41:22.097 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-08-28 12:41:22.097 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:41:22.105 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.98 ms, Average inference time: 7.16 ms

2025-08-28 12:41:22.107 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:41:22.188 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:41:22.314 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch184
2025-08-28 12:41:25.544 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 184/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.574e-03, size: 352, ETA: 2:23:02
2025-08-28 12:41:28.794 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 184/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.574e-03, size: 512, ETA: 2:22:58
2025-08-28 12:41:32.019 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 184/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.573e-03, size: 416, ETA: 2:22:55
2025-08-28 12:41:35.333 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 184/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.572e-03, size: 352, ETA: 2:22:52
2025-08-28 12:41:38.709 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 184/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.572e-03, size: 288, ETA: 2:22:49
2025-08-28 12:41:41.893 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 184/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.571e-03, size: 448, ETA: 2:22:46
2025-08-28 12:41:43.491 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:41:49.667 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:41:52.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:41:54.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5430
2025-08-28 12:41:54.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4754
2025-08-28 12:41:54.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2951
2025-08-28 12:41:54.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4378
2025-08-28 12:41:54.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:41:54.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:41:54.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-08-28 12:41:54.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-08-28 12:41:54.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-08-28 12:41:54.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.438
2025-08-28 12:41:54.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:41:54.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:41:54.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:41:54.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:41:54.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:41:54.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:41:54.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:41:54.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:41:54.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:41:57.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:41:59.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:42:02.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:42:04.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:42:06.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:42:09.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:42:11.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:42:14.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:42:16.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:42:16.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 12:42:16.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:42:16.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:42:16.665 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.96 ms, Average inference time: 7.12 ms

2025-08-28 12:42:16.666 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:42:16.744 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:42:16.827 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch185
2025-08-28 12:42:20.127 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 185/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 8.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 1.0, lr: 1.570e-03, size: 576, ETA: 2:22:42
2025-08-28 12:42:23.428 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 185/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.569e-03, size: 320, ETA: 2:22:39
2025-08-28 12:42:26.611 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 185/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.2, lr: 1.569e-03, size: 416, ETA: 2:22:35
2025-08-28 12:42:29.849 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 185/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.3, lr: 1.568e-03, size: 512, ETA: 2:22:32
2025-08-28 12:42:33.081 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 185/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.567e-03, size: 352, ETA: 2:22:29
2025-08-28 12:42:36.380 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 185/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.567e-03, size: 352, ETA: 2:22:26
2025-08-28 12:42:37.811 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:42:44.346 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:42:46.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:42:48.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5273
2025-08-28 12:42:48.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4817
2025-08-28 12:42:49.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3051
2025-08-28 12:42:49.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4380
2025-08-28 12:42:49.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:42:49.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:42:49.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-08-28 12:42:49.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-28 12:42:49.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.305
2025-08-28 12:42:49.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.438
2025-08-28 12:42:49.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:42:49.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:42:49.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:42:49.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:42:49.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:42:49.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:42:49.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:42:49.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:42:49.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:42:51.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:42:53.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:42:55.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:42:57.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:42:59.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:43:01.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:43:04.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:43:06.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:43:08.275 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:43:08.275 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:43:08.275 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:43:08.276 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:43:08.300 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.39 ms, Average NMS time: 0.98 ms, Average inference time: 7.37 ms

2025-08-28 12:43:08.302 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:43:08.383 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:43:08.471 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch186
2025-08-28 12:43:11.617 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 186/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.566e-03, size: 448, ETA: 2:22:21
2025-08-28 12:43:14.811 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 186/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.565e-03, size: 352, ETA: 2:22:18
2025-08-28 12:43:18.021 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 186/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.564e-03, size: 416, ETA: 2:22:15
2025-08-28 12:43:21.413 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 186/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.564e-03, size: 288, ETA: 2:22:12
2025-08-28 12:43:24.632 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 186/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.563e-03, size: 544, ETA: 2:22:08
2025-08-28 12:43:27.983 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 186/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.562e-03, size: 320, ETA: 2:22:06
2025-08-28 12:43:29.462 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:43:35.642 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:43:38.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:43:40.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5500
2025-08-28 12:43:40.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4616
2025-08-28 12:43:40.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3228
2025-08-28 12:43:40.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4448
2025-08-28 12:43:40.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:43:40.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:43:40.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.550
2025-08-28 12:43:40.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-08-28 12:43:40.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.323
2025-08-28 12:43:40.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.445
2025-08-28 12:43:40.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:43:40.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:43:40.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:43:40.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:43:40.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:43:40.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:43:40.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:43:40.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:43:40.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:43:42.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:43:45.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:43:47.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:43:49.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:43:51.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:43:54.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:43:56.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:43:58.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:44:00.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:44:00.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 12:44:00.840 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:44:00.840 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:44:00.866 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.95 ms, Average inference time: 7.08 ms

2025-08-28 12:44:00.869 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:44:00.946 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:44:01.029 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch187
2025-08-28 12:44:04.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 187/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 1.561e-03, size: 576, ETA: 2:22:01
2025-08-28 12:44:07.581 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 187/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.561e-03, size: 544, ETA: 2:21:58
2025-08-28 12:44:10.916 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 187/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.560e-03, size: 544, ETA: 2:21:55
2025-08-28 12:44:14.237 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 187/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.559e-03, size: 352, ETA: 2:21:52
2025-08-28 12:44:17.516 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 187/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.559e-03, size: 320, ETA: 2:21:49
2025-08-28 12:44:20.589 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 187/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.3, lr: 1.558e-03, size: 256, ETA: 2:21:45
2025-08-28 12:44:21.968 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:44:28.194 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:44:29.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:44:30.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4746
2025-08-28 12:44:30.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4276
2025-08-28 12:44:30.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2974
2025-08-28 12:44:30.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3999
2025-08-28 12:44:30.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:44:30.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:44:30.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-08-28 12:44:30.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-08-28 12:44:30.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.297
2025-08-28 12:44:30.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.400
2025-08-28 12:44:30.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:44:30.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:44:30.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:44:30.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:44:30.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:44:30.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:44:30.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:44:30.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:44:30.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:44:31.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:44:32.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:44:33.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:44:33.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:44:34.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:44:35.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:44:36.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:44:37.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:44:38.418 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:44:38.419 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:44:38.419 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 12:44:38.419 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:44:38.428 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.96 ms, Average inference time: 7.20 ms

2025-08-28 12:44:38.429 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:44:38.575 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:44:38.667 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch188
2025-08-28 12:44:41.826 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 188/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 6.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.557e-03, size: 288, ETA: 2:21:40
2025-08-28 12:44:44.948 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 188/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.556e-03, size: 256, ETA: 2:21:37
2025-08-28 12:44:48.143 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 188/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.556e-03, size: 512, ETA: 2:21:33
2025-08-28 12:44:51.340 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 188/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.3, lr: 1.555e-03, size: 320, ETA: 2:21:30
2025-08-28 12:44:54.538 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 188/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.554e-03, size: 256, ETA: 2:21:27
2025-08-28 12:44:57.744 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 188/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.554e-03, size: 480, ETA: 2:21:24
2025-08-28 12:44:59.234 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:45:05.663 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:45:07.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:45:08.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5510
2025-08-28 12:45:08.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4855
2025-08-28 12:45:09.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3015
2025-08-28 12:45:09.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4460
2025-08-28 12:45:09.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:45:09.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:45:09.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-08-28 12:45:09.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-08-28 12:45:09.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.301
2025-08-28 12:45:09.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.446
2025-08-28 12:45:09.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:45:09.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:45:09.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:45:09.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:45:09.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:45:09.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:45:09.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:45:09.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:45:09.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:45:10.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:45:12.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:45:13.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:45:15.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:45:16.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:45:18.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:45:19.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:45:21.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:45:22.966 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:45:22.966 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 12:45:22.966 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 12:45:22.966 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:45:22.992 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.98 ms, Average inference time: 7.27 ms

2025-08-28 12:45:22.993 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:45:23.077 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:45:23.161 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch189
2025-08-28 12:45:26.154 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 189/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.553e-03, size: 416, ETA: 2:21:19
2025-08-28 12:45:29.264 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 189/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.552e-03, size: 320, ETA: 2:21:15
2025-08-28 12:45:32.431 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 189/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.551e-03, size: 448, ETA: 2:21:12
2025-08-28 12:45:35.721 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 189/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.551e-03, size: 544, ETA: 2:21:09
2025-08-28 12:45:38.995 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 189/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.550e-03, size: 384, ETA: 2:21:06
2025-08-28 12:45:42.179 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 189/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.549e-03, size: 512, ETA: 2:21:02
2025-08-28 12:45:43.683 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:45:49.920 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:45:53.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:45:55.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4861
2025-08-28 12:45:56.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4734
2025-08-28 12:45:56.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2720
2025-08-28 12:45:56.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4105
2025-08-28 12:45:56.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:45:56.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:45:56.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-08-28 12:45:56.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.473
2025-08-28 12:45:56.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.272
2025-08-28 12:45:56.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.410
2025-08-28 12:45:56.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:45:56.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:45:56.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:45:56.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:45:56.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:45:56.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:45:56.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:45:56.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:45:56.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:45:59.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:46:01.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:46:04.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:46:07.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:46:09.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:46:12.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:46:15.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:46:18.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:46:21.049 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:46:21.049 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 12:46:21.049 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-28 12:46:21.049 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:46:21.078 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 1.00 ms, Average inference time: 7.17 ms

2025-08-28 12:46:21.079 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:46:21.156 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:46:21.244 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch190
2025-08-28 12:46:24.379 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 190/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 8.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.7, cls_loss: 0.9, lr: 1.548e-03, size: 544, ETA: 2:20:58
2025-08-28 12:46:27.741 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 190/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.548e-03, size: 384, ETA: 2:20:55
2025-08-28 12:46:30.902 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 190/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.547e-03, size: 544, ETA: 2:20:51
2025-08-28 12:46:34.242 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 190/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.546e-03, size: 512, ETA: 2:20:48
2025-08-28 12:46:37.508 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 190/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.546e-03, size: 288, ETA: 2:20:45
2025-08-28 12:46:40.839 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 190/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.545e-03, size: 416, ETA: 2:20:42
2025-08-28 12:46:42.237 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:46:48.492 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:46:50.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:46:51.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5458
2025-08-28 12:46:52.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4577
2025-08-28 12:46:52.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3263
2025-08-28 12:46:52.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4433
2025-08-28 12:46:52.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:46:52.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:46:52.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.546
2025-08-28 12:46:52.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.458
2025-08-28 12:46:52.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.326
2025-08-28 12:46:52.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.443
2025-08-28 12:46:52.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:46:52.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:46:52.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:46:52.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:46:52.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:46:52.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:46:52.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:46:52.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:46:52.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:46:53.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:46:55.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:46:56.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:46:58.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:47:00.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:47:01.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:47:03.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:47:05.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:47:06.768 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:47:06.769 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 12:47:06.769 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:47:06.769 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:47:06.794 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.97 ms, Average inference time: 7.18 ms

2025-08-28 12:47:06.795 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:47:06.873 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:47:06.953 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch191
2025-08-28 12:47:10.024 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 191/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.544e-03, size: 384, ETA: 2:20:37
2025-08-28 12:47:13.183 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 191/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 1.0, lr: 1.543e-03, size: 320, ETA: 2:20:34
2025-08-28 12:47:16.314 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 191/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.3, cls_loss: 0.9, lr: 1.543e-03, size: 512, ETA: 2:20:30
2025-08-28 12:47:19.677 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 191/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.542e-03, size: 416, ETA: 2:20:27
2025-08-28 12:47:22.856 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 191/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.541e-03, size: 448, ETA: 2:20:24
2025-08-28 12:47:26.108 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 191/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.541e-03, size: 480, ETA: 2:20:21
2025-08-28 12:47:27.676 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:47:33.908 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:47:36.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:47:37.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5406
2025-08-28 12:47:38.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4688
2025-08-28 12:47:38.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3079
2025-08-28 12:47:38.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4391
2025-08-28 12:47:38.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:47:38.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:47:38.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-08-28 12:47:38.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-08-28 12:47:38.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.308
2025-08-28 12:47:38.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.439
2025-08-28 12:47:38.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:47:38.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:47:38.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:47:38.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:47:38.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:47:38.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:47:38.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:47:38.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:47:38.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:47:40.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:47:42.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:47:43.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:47:45.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:47:47.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:47:49.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:47:51.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:47:53.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:47:55.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:47:55.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:47:55.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:47:55.528 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:47:55.552 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 1.01 ms, Average inference time: 7.17 ms

2025-08-28 12:47:55.553 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:47:55.638 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:47:55.740 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch192
2025-08-28 12:47:59.093 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 192/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.540e-03, size: 576, ETA: 2:20:17
2025-08-28 12:48:02.485 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 192/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.539e-03, size: 576, ETA: 2:20:14
2025-08-28 12:48:05.822 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 192/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.538e-03, size: 480, ETA: 2:20:11
2025-08-28 12:48:09.115 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 192/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.538e-03, size: 512, ETA: 2:20:08
2025-08-28 12:48:12.399 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 192/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.537e-03, size: 512, ETA: 2:20:05
2025-08-28 12:48:15.539 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 192/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.536e-03, size: 256, ETA: 2:20:01
2025-08-28 12:48:16.972 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:48:23.259 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:48:26.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:48:29.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5520
2025-08-28 12:48:29.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4488
2025-08-28 12:48:29.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3141
2025-08-28 12:48:29.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4383
2025-08-28 12:48:29.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:48:29.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:48:29.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.552
2025-08-28 12:48:29.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-08-28 12:48:29.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-08-28 12:48:29.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.438
2025-08-28 12:48:29.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:48:29.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:48:29.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:48:29.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:48:29.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:48:29.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:48:29.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:48:29.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:48:29.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:48:32.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:48:35.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:48:38.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:48:41.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:48:44.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:48:47.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:48:50.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:48:53.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:48:56.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:48:56.331 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:48:56.331 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:48:56.331 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:48:56.359 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.96 ms, Average inference time: 7.17 ms

2025-08-28 12:48:56.361 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:48:56.442 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:48:56.524 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch193
2025-08-28 12:48:59.815 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 193/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.535e-03, size: 416, ETA: 2:19:57
2025-08-28 12:49:03.148 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 193/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.534e-03, size: 288, ETA: 2:19:54
2025-08-28 12:49:06.572 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 193/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.534e-03, size: 288, ETA: 2:19:51
2025-08-28 12:49:09.968 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 193/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.533e-03, size: 416, ETA: 2:19:48
2025-08-28 12:49:13.272 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 193/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.532e-03, size: 448, ETA: 2:19:45
2025-08-28 12:49:16.687 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 193/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.169s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.532e-03, size: 352, ETA: 2:19:42
2025-08-28 12:49:18.169 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:49:24.500 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:49:27.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:49:29.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5247
2025-08-28 12:49:30.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4442
2025-08-28 12:49:30.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2689
2025-08-28 12:49:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4126
2025-08-28 12:49:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:49:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:49:30.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 12:49:30.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-28 12:49:30.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.269
2025-08-28 12:49:30.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.413
2025-08-28 12:49:30.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:49:30.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:49:30.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:49:30.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:49:30.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:49:30.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:49:30.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:49:30.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:49:30.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:49:32.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:49:35.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:49:37.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:49:40.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:49:42.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:49:45.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:49:47.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:49:50.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:49:52.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:49:52.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 12:49:52.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-28 12:49:52.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:49:52.603 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.96 ms, Average inference time: 7.18 ms

2025-08-28 12:49:52.604 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:49:52.745 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:49:52.834 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch194
2025-08-28 12:49:56.020 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 194/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.531e-03, size: 448, ETA: 2:19:38
2025-08-28 12:49:59.348 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 194/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.530e-03, size: 448, ETA: 2:19:34
2025-08-28 12:50:02.534 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 194/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.529e-03, size: 384, ETA: 2:19:31
2025-08-28 12:50:05.772 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 194/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.529e-03, size: 320, ETA: 2:19:28
2025-08-28 12:50:08.991 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 194/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.528e-03, size: 576, ETA: 2:19:25
2025-08-28 12:50:12.258 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 194/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.527e-03, size: 416, ETA: 2:19:22
2025-08-28 12:50:13.703 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:50:19.899 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:50:23.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:50:25.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5564
2025-08-28 12:50:25.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4840
2025-08-28 12:50:25.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2959
2025-08-28 12:50:25.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4454
2025-08-28 12:50:25.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:50:25.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:50:25.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.556
2025-08-28 12:50:25.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-28 12:50:25.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.296
2025-08-28 12:50:25.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.445
2025-08-28 12:50:25.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:50:25.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:50:25.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:50:25.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:50:25.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:50:25.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:50:25.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:50:25.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:50:25.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:50:28.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:50:30.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:50:33.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:50:36.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:50:38.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:50:41.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:50:43.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:50:46.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:50:48.757 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:50:48.757 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 12:50:48.757 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 12:50:48.757 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:50:48.785 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.98 ms, Average inference time: 7.13 ms

2025-08-28 12:50:48.786 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:50:48.862 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:50:48.946 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch195
2025-08-28 12:50:52.090 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 195/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.526e-03, size: 448, ETA: 2:19:17
2025-08-28 12:50:55.456 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 195/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.526e-03, size: 352, ETA: 2:19:14
2025-08-28 12:50:58.819 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 195/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.8, lr: 1.525e-03, size: 288, ETA: 2:19:11
2025-08-28 12:51:02.022 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 195/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.2, lr: 1.524e-03, size: 256, ETA: 2:19:08
2025-08-28 12:51:05.165 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 195/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.524e-03, size: 512, ETA: 2:19:04
2025-08-28 12:51:08.615 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 195/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.523e-03, size: 512, ETA: 2:19:01
2025-08-28 12:51:10.099 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:51:16.322 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:51:18.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:51:18.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5005
2025-08-28 12:51:19.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4390
2025-08-28 12:51:19.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2882
2025-08-28 12:51:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4092
2025-08-28 12:51:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:51:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:51:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-08-28 12:51:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-08-28 12:51:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.288
2025-08-28 12:51:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.409
2025-08-28 12:51:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:51:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:51:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:51:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:51:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:51:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:51:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:51:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:51:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:51:20.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:51:22.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:51:23.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:51:24.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:51:26.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:51:27.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:51:28.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:51:30.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:51:31.390 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:51:31.391 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-08-28 12:51:31.391 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-28 12:51:31.391 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:51:31.401 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.08 ms, Average NMS time: 0.95 ms, Average inference time: 7.03 ms

2025-08-28 12:51:31.402 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:51:31.478 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:51:31.564 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch196
2025-08-28 12:51:34.840 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 196/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.522e-03, size: 288, ETA: 2:18:57
2025-08-28 12:51:38.221 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 196/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.521e-03, size: 352, ETA: 2:18:54
2025-08-28 12:51:41.428 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 196/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.520e-03, size: 480, ETA: 2:18:51
2025-08-28 12:51:44.780 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 196/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.520e-03, size: 512, ETA: 2:18:48
2025-08-28 12:51:47.989 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 196/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.519e-03, size: 256, ETA: 2:18:45
2025-08-28 12:51:51.262 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 196/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.518e-03, size: 448, ETA: 2:18:41
2025-08-28 12:51:52.747 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:51:58.928 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:52:03.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:52:06.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5303
2025-08-28 12:52:06.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4836
2025-08-28 12:52:06.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3115
2025-08-28 12:52:06.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4418
2025-08-28 12:52:06.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:52:06.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:52:06.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 12:52:06.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-28 12:52:06.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-08-28 12:52:06.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.442
2025-08-28 12:52:06.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:52:06.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:52:06.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:52:06.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:52:06.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:52:06.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:52:06.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:52:06.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:52:06.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:52:09.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:52:13.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:52:16.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:52:20.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:52:23.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:52:27.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:52:30.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:52:34.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:52:37.503 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:52:37.504 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 12:52:37.504 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:52:37.504 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:52:37.530 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.99 ms, Average inference time: 7.13 ms

2025-08-28 12:52:37.531 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:52:37.649 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:52:37.733 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch197
2025-08-28 12:52:40.943 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 197/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.517e-03, size: 416, ETA: 2:18:37
2025-08-28 12:52:44.170 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 197/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.1, lr: 1.517e-03, size: 544, ETA: 2:18:34
2025-08-28 12:52:47.463 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 197/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 0.8, lr: 1.516e-03, size: 576, ETA: 2:18:31
2025-08-28 12:52:50.810 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 197/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.515e-03, size: 384, ETA: 2:18:28
2025-08-28 12:52:53.986 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 197/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.515e-03, size: 384, ETA: 2:18:24
2025-08-28 12:52:57.156 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 197/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.514e-03, size: 480, ETA: 2:18:21
2025-08-28 12:52:58.609 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:53:04.990 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:53:08.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:53:11.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5338
2025-08-28 12:53:11.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4573
2025-08-28 12:53:11.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3048
2025-08-28 12:53:11.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4320
2025-08-28 12:53:11.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:53:11.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:53:11.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.534
2025-08-28 12:53:11.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-28 12:53:11.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.305
2025-08-28 12:53:11.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.432
2025-08-28 12:53:11.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:53:11.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:53:11.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:53:11.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:53:11.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:53:11.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:53:11.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:53:11.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:53:11.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:53:14.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:53:17.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:53:20.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:53:23.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:53:26.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:53:29.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:53:32.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:53:35.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:53:38.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:53:38.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-08-28 12:53:38.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:53:38.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:53:38.187 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.99 ms, Average inference time: 7.25 ms

2025-08-28 12:53:38.188 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:53:38.264 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:53:38.370 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch198
2025-08-28 12:53:41.440 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 198/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.513e-03, size: 352, ETA: 2:18:16
2025-08-28 12:53:44.674 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 198/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.512e-03, size: 512, ETA: 2:18:13
2025-08-28 12:53:47.844 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 198/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.511e-03, size: 416, ETA: 2:18:09
2025-08-28 12:53:51.142 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 198/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.511e-03, size: 512, ETA: 2:18:06
2025-08-28 12:53:54.471 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 198/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.510e-03, size: 288, ETA: 2:18:03
2025-08-28 12:53:57.756 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 198/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 1.509e-03, size: 544, ETA: 2:18:00
2025-08-28 12:53:59.285 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:54:05.624 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:54:08.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:54:09.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5532
2025-08-28 12:54:10.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4894
2025-08-28 12:54:10.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2963
2025-08-28 12:54:10.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4463
2025-08-28 12:54:10.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:54:10.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:54:10.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.553
2025-08-28 12:54:10.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-08-28 12:54:10.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.296
2025-08-28 12:54:10.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.446
2025-08-28 12:54:10.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:54:10.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:54:10.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:54:10.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:54:10.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:54:10.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:54:10.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:54:10.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:54:10.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:54:12.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:54:14.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:54:16.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:54:18.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:54:20.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:54:22.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:54:24.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:54:26.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:54:28.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:54:28.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:54:28.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 12:54:28.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:54:28.979 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.97 ms, Average inference time: 7.17 ms

2025-08-28 12:54:28.981 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:54:29.060 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:54:29.148 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch199
2025-08-28 12:54:32.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 199/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 7.2, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.508e-03, size: 320, ETA: 2:17:55
2025-08-28 12:54:35.454 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 199/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.508e-03, size: 448, ETA: 2:17:52
2025-08-28 12:54:38.614 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 199/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.507e-03, size: 416, ETA: 2:17:49
2025-08-28 12:54:41.885 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 199/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.506e-03, size: 416, ETA: 2:17:46
2025-08-28 12:54:45.067 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 199/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.8, lr: 1.506e-03, size: 480, ETA: 2:17:42
2025-08-28 12:54:48.414 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 199/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.505e-03, size: 320, ETA: 2:17:39
2025-08-28 12:54:49.833 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:54:56.277 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:54:58.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:54:59.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5283
2025-08-28 12:54:59.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4567
2025-08-28 12:54:59.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2549
2025-08-28 12:54:59.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4133
2025-08-28 12:54:59.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:54:59.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:54:59.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-08-28 12:54:59.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-28 12:54:59.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.255
2025-08-28 12:54:59.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.413
2025-08-28 12:54:59.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:54:59.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:54:59.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:54:59.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:54:59.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:54:59.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:54:59.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:54:59.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:54:59.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:55:00.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:55:02.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:55:03.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:55:04.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:55:06.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:55:07.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:55:09.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:55:10.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:55:11.910 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:55:11.910 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:55:11.910 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-08-28 12:55:11.911 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:55:11.920 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.95 ms, Average inference time: 7.25 ms

2025-08-28 12:55:11.922 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:55:12.002 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:55:12.085 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch200
2025-08-28 12:55:12.086 | INFO     | yolox_microbt.core.trainer:before_epoch:208 - --->No mosaic aug now!
2025-08-28 12:55:12.086 | INFO     | yolox_microbt.core.trainer:before_epoch:210 - --->Add additional L1 loss now!
2025-08-28 12:55:12.086 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:55:15.221 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 200/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.6, iou_loss: 2.8, l1_loss: 1.4, conf_loss: 3.5, cls_loss: 0.8, lr: 1.504e-03, size: 480, ETA: 2:17:34
2025-08-28 12:55:18.382 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 200/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 0.7, lr: 1.503e-03, size: 512, ETA: 2:17:31
2025-08-28 12:55:21.419 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 200/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.502e-03, size: 352, ETA: 2:17:27
2025-08-28 12:55:24.422 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 200/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 0.6, lr: 1.502e-03, size: 416, ETA: 2:17:24
2025-08-28 12:55:27.362 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 200/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 1.501e-03, size: 352, ETA: 2:17:20
2025-08-28 12:55:30.409 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 200/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.500e-03, size: 384, ETA: 2:17:16
2025-08-28 12:55:31.801 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:55:37.998 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:55:39.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:55:39.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4929
2025-08-28 12:55:40.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4125
2025-08-28 12:55:40.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2952
2025-08-28 12:55:40.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4002
2025-08-28 12:55:40.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:55:40.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:55:40.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-08-28 12:55:40.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-08-28 12:55:40.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-08-28 12:55:40.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.400
2025-08-28 12:55:40.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:55:40.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:55:40.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:55:40.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:55:40.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:55:40.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:55:40.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:55:40.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:55:40.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:55:40.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:55:41.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:55:42.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:55:43.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:55:44.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:55:45.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:55:46.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:55:47.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:55:47.966 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:55:47.966 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 12:55:47.966 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 12:55:47.966 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:55:47.974 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.91 ms, Average inference time: 7.13 ms

2025-08-28 12:55:47.975 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:55:48.053 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:55:48.133 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch201
2025-08-28 12:55:50.980 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 201/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.7, cls_loss: 0.6, lr: 1.499e-03, size: 352, ETA: 2:17:11
2025-08-28 12:55:53.949 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 201/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 1.499e-03, size: 416, ETA: 2:17:07
2025-08-28 12:55:56.942 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 201/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.7, lr: 1.498e-03, size: 512, ETA: 2:17:03
2025-08-28 12:55:59.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 201/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.8, lr: 1.497e-03, size: 320, ETA: 2:17:00
2025-08-28 12:56:03.082 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 201/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.496e-03, size: 576, ETA: 2:16:56
2025-08-28 12:56:06.018 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 201/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 1.496e-03, size: 288, ETA: 2:16:52
2025-08-28 12:56:07.357 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:56:13.458 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:56:14.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:56:15.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5342
2025-08-28 12:56:15.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4442
2025-08-28 12:56:15.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3028
2025-08-28 12:56:15.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4271
2025-08-28 12:56:15.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:56:15.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:56:15.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.534
2025-08-28 12:56:15.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-28 12:56:15.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-08-28 12:56:15.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.427
2025-08-28 12:56:15.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:56:15.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:56:15.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:56:15.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:56:15.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:56:15.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:56:15.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:56:15.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:56:15.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:56:16.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:56:17.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:56:17.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:56:18.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:56:19.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:56:20.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:56:20.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:56:21.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:56:22.531 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:56:22.531 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:56:22.531 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 12:56:22.531 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:56:22.539 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.94 ms, Average inference time: 7.09 ms

2025-08-28 12:56:22.540 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:56:22.627 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:56:22.709 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch202
2025-08-28 12:56:25.523 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 202/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 1.495e-03, size: 544, ETA: 2:16:47
2025-08-28 12:56:28.657 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 202/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.494e-03, size: 576, ETA: 2:16:43
2025-08-28 12:56:31.782 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 202/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 1.493e-03, size: 480, ETA: 2:16:40
2025-08-28 12:56:34.839 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 202/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.493e-03, size: 416, ETA: 2:16:36
2025-08-28 12:56:37.932 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 202/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.492e-03, size: 352, ETA: 2:16:33
2025-08-28 12:56:41.003 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 202/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.6, lr: 1.491e-03, size: 512, ETA: 2:16:29
2025-08-28 12:56:42.382 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:56:48.676 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:56:49.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:56:50.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5475
2025-08-28 12:56:50.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4817
2025-08-28 12:56:50.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2995
2025-08-28 12:56:50.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4429
2025-08-28 12:56:50.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:56:50.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:56:50.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.548
2025-08-28 12:56:50.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-28 12:56:50.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.300
2025-08-28 12:56:50.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.443
2025-08-28 12:56:50.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:56:50.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:56:50.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:56:50.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:56:50.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:56:50.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:56:50.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:56:50.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:56:50.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:56:51.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:56:52.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:56:53.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:56:54.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:56:55.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:56:55.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:56:56.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:56:57.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:56:58.535 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:56:58.535 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 12:56:58.535 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:56:58.535 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:56:58.543 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.94 ms, Average inference time: 7.06 ms

2025-08-28 12:56:58.544 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:56:58.674 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:56:58.783 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch203
2025-08-28 12:57:01.654 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 203/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.490e-03, size: 448, ETA: 2:16:24
2025-08-28 12:57:04.752 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 203/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 10.4, iou_loss: 3.1, l1_loss: 2.1, conf_loss: 4.3, cls_loss: 0.9, lr: 1.489e-03, size: 576, ETA: 2:16:20
2025-08-28 12:57:07.813 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 203/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.3, cls_loss: 0.6, lr: 1.489e-03, size: 544, ETA: 2:16:17
2025-08-28 12:57:10.866 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 203/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.488e-03, size: 256, ETA: 2:16:13
2025-08-28 12:57:13.835 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 203/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 1.487e-03, size: 352, ETA: 2:16:10
2025-08-28 12:57:16.799 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 203/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 1.487e-03, size: 416, ETA: 2:16:06
2025-08-28 12:57:18.112 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:57:24.304 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:57:26.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:57:27.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5306
2025-08-28 12:57:27.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4493
2025-08-28 12:57:27.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2841
2025-08-28 12:57:27.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4213
2025-08-28 12:57:27.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:57:27.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:57:27.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-08-28 12:57:27.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-08-28 12:57:27.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.284
2025-08-28 12:57:27.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.421
2025-08-28 12:57:27.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:57:27.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:57:27.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:57:27.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:57:27.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:57:27.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:57:27.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:57:27.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:57:27.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:57:29.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:57:30.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:57:32.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:57:33.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:57:35.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:57:36.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:57:38.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:57:39.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:57:40.989 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:57:40.989 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:57:40.990 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 12:57:40.990 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:57:41.014 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.95 ms, Average inference time: 7.16 ms

2025-08-28 12:57:41.015 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:57:41.104 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:57:41.186 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch204
2025-08-28 12:57:44.313 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 204/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.7, lr: 1.486e-03, size: 416, ETA: 2:16:01
2025-08-28 12:57:47.325 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 204/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.2, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 2.5, cls_loss: 0.8, lr: 1.485e-03, size: 288, ETA: 2:15:57
2025-08-28 12:57:50.342 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 204/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.7, lr: 1.484e-03, size: 416, ETA: 2:15:54
2025-08-28 12:57:53.328 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 204/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 1.3, cls_loss: 0.6, lr: 1.483e-03, size: 416, ETA: 2:15:50
2025-08-28 12:57:56.342 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 204/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.8, lr: 1.483e-03, size: 256, ETA: 2:15:46
2025-08-28 12:57:59.259 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 204/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.7, lr: 1.482e-03, size: 512, ETA: 2:15:43
2025-08-28 12:58:00.598 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:58:06.982 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:58:08.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:58:08.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5475
2025-08-28 12:58:08.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4869
2025-08-28 12:58:08.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3832
2025-08-28 12:58:08.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4725
2025-08-28 12:58:08.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:58:08.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:58:08.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.547
2025-08-28 12:58:08.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-08-28 12:58:08.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-28 12:58:08.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.473
2025-08-28 12:58:08.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:58:08.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:58:08.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:58:08.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:58:08.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:58:08.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:58:08.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:58:08.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:58:08.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:58:09.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:58:10.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:58:11.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:58:12.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:58:13.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:58:14.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:58:14.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:58:15.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:58:16.642 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:58:16.642 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 12:58:16.642 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 12:58:16.642 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:58:16.649 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.96 ms, Average inference time: 7.18 ms

2025-08-28 12:58:16.651 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:58:16.769 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:58:16.872 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch205
2025-08-28 12:58:19.933 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 205/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.3, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 3.3, cls_loss: 0.6, lr: 1.481e-03, size: 384, ETA: 2:15:37
2025-08-28 12:58:22.825 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 205/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 1.480e-03, size: 352, ETA: 2:15:34
2025-08-28 12:58:25.839 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 205/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 1.480e-03, size: 384, ETA: 2:15:30
2025-08-28 12:58:28.877 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 205/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 1.479e-03, size: 352, ETA: 2:15:26
2025-08-28 12:58:31.940 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 205/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 1.478e-03, size: 416, ETA: 2:15:23
2025-08-28 12:58:35.057 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 205/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.8, lr: 1.477e-03, size: 576, ETA: 2:15:19
2025-08-28 12:58:36.567 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:58:42.729 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:58:43.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:58:44.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5505
2025-08-28 12:58:44.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4639
2025-08-28 12:58:44.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3158
2025-08-28 12:58:44.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4434
2025-08-28 12:58:44.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:58:44.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:58:44.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-08-28 12:58:44.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.464
2025-08-28 12:58:44.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.316
2025-08-28 12:58:44.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.443
2025-08-28 12:58:44.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:58:44.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:58:44.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:58:44.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:58:44.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:58:44.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:58:44.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:58:44.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:58:44.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:58:45.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:58:45.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:58:46.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:58:47.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:58:47.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:58:48.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:58:49.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:58:50.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:58:50.801 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:58:50.801 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 12:58:50.801 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 12:58:50.802 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:58:50.809 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.94 ms, Average inference time: 7.09 ms

2025-08-28 12:58:50.810 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:58:50.929 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:58:51.005 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch206
2025-08-28 12:58:53.790 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 206/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.137s, data_time: 0.002s, total_loss: 8.3, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.476e-03, size: 320, ETA: 2:15:14
2025-08-28 12:58:56.806 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 206/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.8, lr: 1.476e-03, size: 352, ETA: 2:15:10
2025-08-28 12:58:59.779 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 206/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.8, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 3.4, cls_loss: 1.2, lr: 1.475e-03, size: 416, ETA: 2:15:07
2025-08-28 12:59:02.908 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 206/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 9.5, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 4.4, cls_loss: 1.2, lr: 1.474e-03, size: 256, ETA: 2:15:03
2025-08-28 12:59:05.810 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 206/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 1.474e-03, size: 288, ETA: 2:15:00
2025-08-28 12:59:08.756 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 206/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.473e-03, size: 352, ETA: 2:14:56
2025-08-28 12:59:10.169 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:59:16.407 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:59:17.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:59:18.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5614
2025-08-28 12:59:18.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4925
2025-08-28 12:59:18.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3721
2025-08-28 12:59:18.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4753
2025-08-28 12:59:18.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:59:18.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:59:18.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.561
2025-08-28 12:59:18.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.492
2025-08-28 12:59:18.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-28 12:59:18.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.475
2025-08-28 12:59:18.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:59:18.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:59:18.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:59:18.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:59:18.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:59:18.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:59:18.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:59:18.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:59:18.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:59:19.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:59:19.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:59:20.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:59:21.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:59:22.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:59:23.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:59:23.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:59:24.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:59:25.704 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:59:25.704 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 12:59:25.704 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 12:59:25.704 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:59:25.712 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.94 ms, Average inference time: 7.07 ms

2025-08-28 12:59:25.720 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:59:25.890 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:59:25.963 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch207
2025-08-28 12:59:28.890 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 207/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.003s, total_loss: 8.3, iou_loss: 3.3, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.8, lr: 1.472e-03, size: 448, ETA: 2:14:51
2025-08-28 12:59:31.896 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 207/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.5, conf_loss: 2.4, cls_loss: 0.6, lr: 1.471e-03, size: 288, ETA: 2:14:47
2025-08-28 12:59:35.020 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 207/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.5, lr: 1.470e-03, size: 384, ETA: 2:14:44
2025-08-28 12:59:37.961 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 207/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.8, lr: 1.470e-03, size: 480, ETA: 2:14:40
2025-08-28 12:59:40.933 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 207/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.469e-03, size: 256, ETA: 2:14:36
2025-08-28 12:59:43.908 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 207/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 1.468e-03, size: 320, ETA: 2:14:32
2025-08-28 12:59:45.221 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:59:51.269 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 12:59:52.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 12:59:52.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5507
2025-08-28 12:59:52.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4666
2025-08-28 12:59:52.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3710
2025-08-28 12:59:52.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4628
2025-08-28 12:59:52.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 12:59:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 12:59:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-08-28 12:59:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.467
2025-08-28 12:59:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-08-28 12:59:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.463
2025-08-28 12:59:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 12:59:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 12:59:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 12:59:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 12:59:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 12:59:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 12:59:52.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 12:59:52.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 12:59:52.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 12:59:53.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 12:59:53.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 12:59:54.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 12:59:55.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 12:59:55.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 12:59:56.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 12:59:57.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 12:59:57.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 12:59:58.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 12:59:58.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 12:59:58.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 12:59:58.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 12:59:58.331 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.94 ms, Average inference time: 7.13 ms

2025-08-28 12:59:58.333 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:59:58.464 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 12:59:58.537 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch208
2025-08-28 13:00:01.413 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 208/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.467e-03, size: 384, ETA: 2:14:27
2025-08-28 13:00:04.463 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 208/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 1.466e-03, size: 416, ETA: 2:14:23
2025-08-28 13:00:07.431 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 208/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.466e-03, size: 416, ETA: 2:14:20
2025-08-28 13:00:10.400 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 208/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 1.465e-03, size: 320, ETA: 2:14:16
2025-08-28 13:00:13.339 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 208/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 1.3, cls_loss: 0.7, lr: 1.464e-03, size: 320, ETA: 2:14:12
2025-08-28 13:00:16.425 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 208/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.5, cls_loss: 0.7, lr: 1.464e-03, size: 320, ETA: 2:14:09
2025-08-28 13:00:17.850 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:00:24.005 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:00:24.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:00:25.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5521
2025-08-28 13:00:25.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4535
2025-08-28 13:00:25.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3689
2025-08-28 13:00:25.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4582
2025-08-28 13:00:25.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:00:25.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:00:25.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.552
2025-08-28 13:00:25.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.454
2025-08-28 13:00:25.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-28 13:00:25.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.458
2025-08-28 13:00:25.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:00:25.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:00:25.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:00:25.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:00:25.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:00:25.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:00:25.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:00:25.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:00:25.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:00:26.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:00:26.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:00:27.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:00:28.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:00:28.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:00:29.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:00:29.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:00:30.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:00:31.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:00:31.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:00:31.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:00:31.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:00:31.023 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.92 ms, Average inference time: 7.10 ms

2025-08-28 13:00:31.024 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:00:31.108 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:00:31.186 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch209
2025-08-28 13:00:34.005 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 209/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.138s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.3, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 0.8, lr: 1.463e-03, size: 320, ETA: 2:14:03
2025-08-28 13:00:37.142 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 209/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.462e-03, size: 576, ETA: 2:14:00
2025-08-28 13:00:40.329 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 209/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.7, lr: 1.461e-03, size: 544, ETA: 2:13:57
2025-08-28 13:00:43.492 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 209/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.460e-03, size: 544, ETA: 2:13:54
2025-08-28 13:00:46.583 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 209/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.460e-03, size: 288, ETA: 2:13:50
2025-08-28 13:00:49.587 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 209/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 17.6, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 17.6, cls_loss: 0.0, lr: 1.459e-03, size: 384, ETA: 2:13:46
2025-08-28 13:00:51.059 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:00:57.244 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:00:57.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:00:58.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5480
2025-08-28 13:00:58.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4796
2025-08-28 13:00:58.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3291
2025-08-28 13:00:58.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4523
2025-08-28 13:00:58.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:00:58.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:00:58.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.548
2025-08-28 13:00:58.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.480
2025-08-28 13:00:58.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.329
2025-08-28 13:00:58.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.452
2025-08-28 13:00:58.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:00:58.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:00:58.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:00:58.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:00:58.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:00:58.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:00:58.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:00:58.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:00:58.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:00:58.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:00:59.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:00:59.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:01:00.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:01:00.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:01:01.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:01:01.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:01:02.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:01:02.540 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:01:02.541 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:01:02.541 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 13:01:02.541 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:01:02.548 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.91 ms, Average inference time: 7.05 ms

2025-08-28 13:01:02.549 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:01:02.630 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:01:02.710 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch210
2025-08-28 13:01:05.591 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 210/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 9.3, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 4.3, cls_loss: 0.9, lr: 1.458e-03, size: 448, ETA: 2:13:41
2025-08-28 13:01:08.585 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 210/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.9, lr: 1.457e-03, size: 384, ETA: 2:13:38
2025-08-28 13:01:11.557 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 210/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.456e-03, size: 384, ETA: 2:13:34
2025-08-28 13:01:14.706 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 210/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.4, conf_loss: 2.9, cls_loss: 0.7, lr: 1.456e-03, size: 576, ETA: 2:13:31
2025-08-28 13:01:17.982 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 210/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 1.455e-03, size: 576, ETA: 2:13:28
2025-08-28 13:01:21.281 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 210/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.7, lr: 1.454e-03, size: 288, ETA: 2:13:24
2025-08-28 13:01:22.674 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:01:28.787 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:01:29.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:01:30.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5309
2025-08-28 13:01:30.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4865
2025-08-28 13:01:30.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2792
2025-08-28 13:01:30.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4322
2025-08-28 13:01:30.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:01:30.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:01:30.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-08-28 13:01:30.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-08-28 13:01:30.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.279
2025-08-28 13:01:30.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.432
2025-08-28 13:01:30.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:01:30.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:01:30.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:01:30.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:01:30.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:01:30.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:01:30.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:01:30.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:01:30.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:01:31.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:01:32.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:01:33.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:01:34.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:01:34.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:01:35.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:01:36.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:01:37.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:01:38.283 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:01:38.284 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 13:01:38.284 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 13:01:38.284 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:01:38.291 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.96 ms, Average inference time: 7.14 ms

2025-08-28 13:01:38.292 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:01:38.372 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:01:38.451 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch211
2025-08-28 13:01:41.298 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 211/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 9.0, iou_loss: 3.2, l1_loss: 1.4, conf_loss: 3.5, cls_loss: 0.9, lr: 1.453e-03, size: 544, ETA: 2:13:19
2025-08-28 13:01:44.314 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 211/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 1.453e-03, size: 352, ETA: 2:13:15
2025-08-28 13:01:47.250 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 211/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.9, lr: 1.452e-03, size: 288, ETA: 2:13:12
2025-08-28 13:01:50.431 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 211/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.5, lr: 1.451e-03, size: 512, ETA: 2:13:08
2025-08-28 13:01:53.447 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 211/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 1.450e-03, size: 352, ETA: 2:13:05
2025-08-28 13:01:56.563 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 211/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 3.7, cls_loss: 0.7, lr: 1.450e-03, size: 480, ETA: 2:13:02
2025-08-28 13:01:57.978 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:02:04.317 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:02:05.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:02:06.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5645
2025-08-28 13:02:06.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5153
2025-08-28 13:02:06.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3801
2025-08-28 13:02:06.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4866
2025-08-28 13:02:06.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:02:06.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:02:06.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.564
2025-08-28 13:02:06.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 13:02:06.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-08-28 13:02:06.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-08-28 13:02:06.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:02:06.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:02:06.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:02:06.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:02:06.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:02:06.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:02:06.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:02:06.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:02:06.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:02:07.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:02:08.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:02:09.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:02:10.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:02:11.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:02:12.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:02:13.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:02:14.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:02:15.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:02:15.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:02:15.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:02:15.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:02:15.984 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.95 ms, Average inference time: 7.10 ms

2025-08-28 13:02:15.985 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:02:16.118 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:02:16.192 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch212
2025-08-28 13:02:19.044 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 212/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 1.449e-03, size: 288, ETA: 2:12:56
2025-08-28 13:02:22.032 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 212/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.448e-03, size: 480, ETA: 2:12:53
2025-08-28 13:02:25.036 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 212/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 1.447e-03, size: 544, ETA: 2:12:49
2025-08-28 13:02:28.070 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 212/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.7, lr: 1.446e-03, size: 352, ETA: 2:12:45
2025-08-28 13:02:31.128 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 212/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 1.446e-03, size: 256, ETA: 2:12:42
2025-08-28 13:02:34.320 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 212/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.445e-03, size: 512, ETA: 2:12:39
2025-08-28 13:02:35.764 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:02:41.839 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:02:42.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:02:43.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5583
2025-08-28 13:02:43.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4935
2025-08-28 13:02:43.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3318
2025-08-28 13:02:43.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4612
2025-08-28 13:02:43.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:02:43.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:02:43.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.558
2025-08-28 13:02:43.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-08-28 13:02:43.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.332
2025-08-28 13:02:43.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.461
2025-08-28 13:02:43.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:02:43.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:02:43.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:02:43.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:02:43.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:02:43.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:02:43.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:02:43.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:02:43.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:02:43.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:02:44.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:02:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:02:45.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:02:46.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:02:46.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:02:47.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:02:48.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:02:48.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:02:48.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:02:48.960 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:02:48.960 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:02:48.967 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.92 ms, Average inference time: 7.07 ms

2025-08-28 13:02:48.969 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:02:49.049 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:02:49.128 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch213
2025-08-28 13:02:52.028 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 213/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 1.444e-03, size: 480, ETA: 2:12:33
2025-08-28 13:02:54.910 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 213/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.9, lr: 1.443e-03, size: 352, ETA: 2:12:30
2025-08-28 13:02:57.923 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 213/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 1.4, conf_loss: 2.7, cls_loss: 0.7, lr: 1.442e-03, size: 416, ETA: 2:12:26
2025-08-28 13:03:00.843 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 213/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 1.442e-03, size: 480, ETA: 2:12:22
2025-08-28 13:03:03.868 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 213/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.6, lr: 1.441e-03, size: 576, ETA: 2:12:19
2025-08-28 13:03:06.919 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 213/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.8, lr: 1.440e-03, size: 416, ETA: 2:12:15
2025-08-28 13:03:08.293 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:03:14.544 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:03:15.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:03:15.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5429
2025-08-28 13:03:15.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4549
2025-08-28 13:03:15.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3537
2025-08-28 13:03:15.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4505
2025-08-28 13:03:15.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:03:15.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:03:15.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-08-28 13:03:15.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-08-28 13:03:15.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.354
2025-08-28 13:03:15.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.451
2025-08-28 13:03:15.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:03:15.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:03:15.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:03:15.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:03:15.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:03:15.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:03:15.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:03:15.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:03:15.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:03:16.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:03:17.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:03:17.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:03:18.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:03:19.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:03:19.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:03:20.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:03:20.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:03:21.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:03:21.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 13:03:21.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 13:03:21.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:03:21.644 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.95 ms, Average inference time: 7.11 ms

2025-08-28 13:03:21.645 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:03:21.761 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:03:21.880 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch214
2025-08-28 13:03:24.728 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 214/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.439e-03, size: 384, ETA: 2:12:10
2025-08-28 13:03:27.867 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 214/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 1.438e-03, size: 480, ETA: 2:12:07
2025-08-28 13:03:30.991 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 214/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.9, lr: 1.438e-03, size: 480, ETA: 2:12:03
2025-08-28 13:03:33.995 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 214/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 8.3, iou_loss: 2.8, l1_loss: 0.7, conf_loss: 3.9, cls_loss: 0.9, lr: 1.437e-03, size: 416, ETA: 2:12:00
2025-08-28 13:03:36.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 214/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 1.436e-03, size: 544, ETA: 2:11:56
2025-08-28 13:03:40.095 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 214/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.6, lr: 1.436e-03, size: 352, ETA: 2:11:53
2025-08-28 13:03:41.510 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:03:47.622 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:03:48.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:03:49.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5655
2025-08-28 13:03:49.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4941
2025-08-28 13:03:49.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3215
2025-08-28 13:03:49.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4604
2025-08-28 13:03:49.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:03:49.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:03:49.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.565
2025-08-28 13:03:49.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-08-28 13:03:49.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.322
2025-08-28 13:03:49.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.460
2025-08-28 13:03:49.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:03:49.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:03:49.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:03:49.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:03:49.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:03:49.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:03:49.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:03:49.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:03:49.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:03:49.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:03:50.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:03:51.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:03:52.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:03:52.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:03:53.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:03:54.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:03:54.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:03:55.550 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:03:55.550 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:03:55.550 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:03:55.551 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:03:55.558 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.91 ms, Average inference time: 7.04 ms

2025-08-28 13:03:55.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:03:55.643 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:03:55.763 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch215
2025-08-28 13:03:58.778 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 215/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.6, lr: 1.435e-03, size: 320, ETA: 2:11:48
2025-08-28 13:04:01.808 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 215/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 1.434e-03, size: 512, ETA: 2:11:44
2025-08-28 13:04:04.758 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 215/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 8.0, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 3.1, cls_loss: 0.7, lr: 1.433e-03, size: 512, ETA: 2:11:40
2025-08-28 13:04:07.726 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 215/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.432e-03, size: 512, ETA: 2:11:37
2025-08-28 13:04:10.867 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 215/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 1.0, lr: 1.432e-03, size: 384, ETA: 2:11:33
2025-08-28 13:04:13.897 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 215/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 1.0, lr: 1.431e-03, size: 448, ETA: 2:11:30
2025-08-28 13:04:15.278 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:04:21.366 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:04:22.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:04:22.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5455
2025-08-28 13:04:23.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4950
2025-08-28 13:04:23.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3269
2025-08-28 13:04:23.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4558
2025-08-28 13:04:23.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:04:23.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:04:23.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.546
2025-08-28 13:04:23.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-08-28 13:04:23.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.327
2025-08-28 13:04:23.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.456
2025-08-28 13:04:23.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:04:23.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:04:23.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:04:23.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:04:23.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:04:23.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:04:23.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:04:23.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:04:23.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:04:23.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:04:24.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:04:25.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:04:26.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:04:26.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:04:27.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:04:28.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:04:29.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:04:30.034 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:04:30.035 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 13:04:30.035 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:04:30.035 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:04:30.042 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.08 ms, Average NMS time: 0.94 ms, Average inference time: 7.02 ms

2025-08-28 13:04:30.043 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:04:30.132 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:04:30.214 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch216
2025-08-28 13:04:33.242 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 216/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.430e-03, size: 288, ETA: 2:11:25
2025-08-28 13:04:36.352 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 216/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.7, lr: 1.429e-03, size: 256, ETA: 2:11:21
2025-08-28 13:04:39.326 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 216/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.428e-03, size: 352, ETA: 2:11:18
2025-08-28 13:04:42.357 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 216/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.8, lr: 1.428e-03, size: 288, ETA: 2:11:14
2025-08-28 13:04:45.305 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 216/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.427e-03, size: 384, ETA: 2:11:11
2025-08-28 13:04:48.262 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 216/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.8, lr: 1.426e-03, size: 256, ETA: 2:11:07
2025-08-28 13:04:49.660 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:04:55.849 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:04:56.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:04:57.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5741
2025-08-28 13:04:57.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5023
2025-08-28 13:04:57.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3636
2025-08-28 13:04:57.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4800
2025-08-28 13:04:57.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:04:57.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:04:57.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-08-28 13:04:57.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-08-28 13:04:57.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-08-28 13:04:57.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.480
2025-08-28 13:04:57.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:04:57.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:04:57.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:04:57.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:04:57.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:04:57.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:04:57.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:04:57.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:04:57.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:04:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:04:59.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:04:59.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:05:00.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:05:01.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:05:02.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:05:02.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:05:03.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:05:04.487 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:05:04.488 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:05:04.488 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:05:04.488 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:05:04.496 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.95 ms, Average inference time: 7.09 ms

2025-08-28 13:05:04.497 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:05:04.589 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:05:04.666 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch217
2025-08-28 13:05:07.697 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 217/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.9, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 4.0, cls_loss: 0.8, lr: 1.425e-03, size: 576, ETA: 2:11:02
2025-08-28 13:05:10.775 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 217/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 1.424e-03, size: 448, ETA: 2:10:59
2025-08-28 13:05:13.919 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 217/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 1.424e-03, size: 384, ETA: 2:10:55
2025-08-28 13:05:16.993 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 217/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.9, lr: 1.423e-03, size: 288, ETA: 2:10:52
2025-08-28 13:05:19.931 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 217/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.6, lr: 1.422e-03, size: 544, ETA: 2:10:48
2025-08-28 13:05:23.030 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 217/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.9, lr: 1.421e-03, size: 256, ETA: 2:10:45
2025-08-28 13:05:24.385 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:05:30.637 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:05:31.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:05:31.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5511
2025-08-28 13:05:31.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5080
2025-08-28 13:05:31.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3973
2025-08-28 13:05:31.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4855
2025-08-28 13:05:31.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:05:31.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:05:31.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-08-28 13:05:31.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.508
2025-08-28 13:05:31.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-08-28 13:05:31.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.485
2025-08-28 13:05:31.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:05:31.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:05:31.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:05:31.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:05:31.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:05:31.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:05:31.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:05:31.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:05:31.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:05:32.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:05:32.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:05:33.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:05:33.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:05:34.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:05:34.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:05:35.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:05:35.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:05:36.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:05:36.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:05:36.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:05:36.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:05:36.436 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.95 ms, Average inference time: 7.18 ms

2025-08-28 13:05:36.437 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:05:36.564 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:05:36.637 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch218
2025-08-28 13:05:39.608 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 218/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 1.420e-03, size: 544, ETA: 2:10:40
2025-08-28 13:05:42.683 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 218/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 1.420e-03, size: 448, ETA: 2:10:36
2025-08-28 13:05:45.749 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 218/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 1.419e-03, size: 448, ETA: 2:10:33
2025-08-28 13:05:48.774 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 218/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.418e-03, size: 256, ETA: 2:10:29
2025-08-28 13:05:51.763 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 218/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.7, lr: 1.417e-03, size: 544, ETA: 2:10:26
2025-08-28 13:05:54.828 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 218/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 1.9, cls_loss: 0.7, lr: 1.417e-03, size: 384, ETA: 2:10:22
2025-08-28 13:05:56.178 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:06:02.287 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:06:02.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:06:03.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5394
2025-08-28 13:06:03.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4829
2025-08-28 13:06:03.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3482
2025-08-28 13:06:03.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4568
2025-08-28 13:06:03.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:06:03.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:06:03.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-08-28 13:06:03.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.483
2025-08-28 13:06:03.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.348
2025-08-28 13:06:03.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.457
2025-08-28 13:06:03.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:06:03.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:06:03.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:06:03.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:06:03.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:06:03.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:06:03.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:06:03.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:06:03.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:06:03.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:06:04.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:06:04.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:06:05.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:06:05.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:06:06.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:06:06.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:06:07.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:06:07.632 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:06:07.632 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:06:07.632 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:06:07.632 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:06:07.639 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.92 ms, Average inference time: 7.21 ms

2025-08-28 13:06:07.640 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:06:07.722 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:06:07.804 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch219
2025-08-28 13:06:10.794 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 219/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 1.416e-03, size: 480, ETA: 2:10:17
2025-08-28 13:06:13.824 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 219/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.6, lr: 1.415e-03, size: 352, ETA: 2:10:13
2025-08-28 13:06:16.870 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 219/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 2.2, cls_loss: 0.7, lr: 1.414e-03, size: 544, ETA: 2:10:10
2025-08-28 13:06:19.932 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 219/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.3, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 3.3, cls_loss: 0.9, lr: 1.413e-03, size: 544, ETA: 2:10:07
2025-08-28 13:06:23.017 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 219/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.9, lr: 1.413e-03, size: 288, ETA: 2:10:03
2025-08-28 13:06:26.080 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 219/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 0.7, lr: 1.412e-03, size: 512, ETA: 2:10:00
2025-08-28 13:06:27.439 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:06:33.592 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:06:34.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:06:34.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5060
2025-08-28 13:06:34.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4348
2025-08-28 13:06:34.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3178
2025-08-28 13:06:34.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4195
2025-08-28 13:06:34.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:06:34.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:06:34.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-08-28 13:06:34.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.435
2025-08-28 13:06:34.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-08-28 13:06:34.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.420
2025-08-28 13:06:34.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:06:34.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:06:34.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:06:34.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:06:34.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:06:34.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:06:34.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:06:34.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:06:34.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:06:34.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:06:35.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:06:35.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:06:36.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:06:36.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:06:36.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:06:37.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:06:37.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:06:37.849 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:06:37.849 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 13:06:37.849 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 13:06:37.849 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:06:37.855 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.82 ms, Average inference time: 7.07 ms

2025-08-28 13:06:37.857 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:06:37.936 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:06:38.015 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch220
2025-08-28 13:06:40.957 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 220/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 1.411e-03, size: 256, ETA: 2:09:54
2025-08-28 13:06:43.973 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 220/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.7, lr: 1.410e-03, size: 352, ETA: 2:09:51
2025-08-28 13:06:47.011 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 220/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.8, lr: 1.409e-03, size: 384, ETA: 2:09:47
2025-08-28 13:06:49.980 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 220/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.8, lr: 1.409e-03, size: 416, ETA: 2:09:44
2025-08-28 13:06:52.900 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 220/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.408e-03, size: 256, ETA: 2:09:40
2025-08-28 13:06:55.879 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 220/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.7, lr: 1.407e-03, size: 448, ETA: 2:09:36
2025-08-28 13:06:57.254 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:07:03.307 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:07:04.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:07:04.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5882
2025-08-28 13:07:04.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4821
2025-08-28 13:07:04.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3761
2025-08-28 13:07:04.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4821
2025-08-28 13:07:04.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:07:04.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:07:04.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-08-28 13:07:04.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-28 13:07:04.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-08-28 13:07:04.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.482
2025-08-28 13:07:04.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:07:04.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:07:04.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:07:04.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:07:04.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:07:04.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:07:04.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:07:04.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:07:04.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:07:05.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:07:06.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:07:06.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:07:07.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:07:08.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:07:08.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:07:09.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:07:09.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:07:10.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:07:10.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:07:10.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:07:10.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:07:10.638 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.94 ms, Average inference time: 7.13 ms

2025-08-28 13:07:10.639 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:07:10.723 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:07:10.800 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch221
2025-08-28 13:07:13.605 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 221/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.6, lr: 1.406e-03, size: 448, ETA: 2:09:31
2025-08-28 13:07:16.729 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 221/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.6, lr: 1.405e-03, size: 320, ETA: 2:09:28
2025-08-28 13:07:19.798 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 221/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 1.3, lr: 1.405e-03, size: 576, ETA: 2:09:24
2025-08-28 13:07:22.923 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 221/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 0.9, lr: 1.404e-03, size: 544, ETA: 2:09:21
2025-08-28 13:07:25.961 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 221/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.403e-03, size: 288, ETA: 2:09:17
2025-08-28 13:07:29.065 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 221/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 1.402e-03, size: 544, ETA: 2:09:14
2025-08-28 13:07:30.510 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:07:36.608 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:07:37.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:07:37.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5416
2025-08-28 13:07:37.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4438
2025-08-28 13:07:37.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3270
2025-08-28 13:07:37.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4375
2025-08-28 13:07:37.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:07:37.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:07:37.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-08-28 13:07:37.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-28 13:07:37.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.327
2025-08-28 13:07:37.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.437
2025-08-28 13:07:37.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:07:37.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:07:37.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:07:37.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:07:37.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:07:37.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:07:37.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:07:37.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:07:37.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:07:38.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:07:38.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:07:39.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:07:39.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:07:40.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:07:40.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:07:40.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:07:41.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:07:41.813 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:07:41.813 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 13:07:41.814 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 13:07:41.814 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:07:41.821 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.90 ms, Average inference time: 7.10 ms

2025-08-28 13:07:41.821 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:07:41.931 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:07:42.007 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch222
2025-08-28 13:07:44.926 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 222/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 9.5, iou_loss: 3.4, l1_loss: 1.4, conf_loss: 3.9, cls_loss: 0.8, lr: 1.401e-03, size: 448, ETA: 2:09:09
2025-08-28 13:07:47.942 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 222/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.8, lr: 1.400e-03, size: 320, ETA: 2:09:05
2025-08-28 13:07:50.880 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 222/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 1.1, lr: 1.400e-03, size: 384, ETA: 2:09:02
2025-08-28 13:07:53.926 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 222/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.6, lr: 1.399e-03, size: 544, ETA: 2:08:58
2025-08-28 13:07:56.895 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 222/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 1.2, lr: 1.398e-03, size: 544, ETA: 2:08:55
2025-08-28 13:07:59.837 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 222/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 1.397e-03, size: 416, ETA: 2:08:51
2025-08-28 13:08:01.201 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:08:07.371 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:08:08.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:08:08.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5455
2025-08-28 13:08:08.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4740
2025-08-28 13:08:08.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3229
2025-08-28 13:08:08.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4475
2025-08-28 13:08:08.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:08:08.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:08:08.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.546
2025-08-28 13:08:08.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-28 13:08:08.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.323
2025-08-28 13:08:08.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.447
2025-08-28 13:08:08.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:08:08.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:08:08.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:08:08.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:08:08.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:08:08.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:08:08.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:08:08.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:08:08.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:08:09.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:08:09.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:08:10.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:08:11.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:08:11.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:08:12.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:08:12.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:08:13.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:08:14.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:08:14.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:08:14.200 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 13:08:14.200 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:08:14.207 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.94 ms, Average inference time: 7.14 ms

2025-08-28 13:08:14.208 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:08:14.289 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:08:14.367 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch223
2025-08-28 13:08:17.294 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 223/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.7, lr: 1.396e-03, size: 288, ETA: 2:08:46
2025-08-28 13:08:20.211 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 223/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 1.396e-03, size: 288, ETA: 2:08:42
2025-08-28 13:08:23.339 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 223/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 3.0, cls_loss: 0.7, lr: 1.395e-03, size: 576, ETA: 2:08:39
2025-08-28 13:08:26.445 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 223/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.8, iou_loss: 1.5, l1_loss: 0.7, conf_loss: 3.0, cls_loss: 0.5, lr: 1.394e-03, size: 544, ETA: 2:08:36
2025-08-28 13:08:29.473 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 223/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.7, lr: 1.393e-03, size: 352, ETA: 2:08:32
2025-08-28 13:08:32.369 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 223/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.5, lr: 1.393e-03, size: 416, ETA: 2:08:28
2025-08-28 13:08:33.703 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:08:39.936 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:08:40.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:08:41.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5513
2025-08-28 13:08:41.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4809
2025-08-28 13:08:41.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3905
2025-08-28 13:08:41.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4742
2025-08-28 13:08:41.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:08:41.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:08:41.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-08-28 13:08:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-28 13:08:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-28 13:08:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.474
2025-08-28 13:08:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:08:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:08:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:08:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:08:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:08:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:08:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:08:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:08:41.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:08:42.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:08:42.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:08:43.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:08:44.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:08:44.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:08:45.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:08:46.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:08:46.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:08:47.351 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:08:47.351 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:08:47.351 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:08:47.351 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:08:47.359 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.95 ms, Average inference time: 7.14 ms

2025-08-28 13:08:47.360 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:08:47.498 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:08:47.577 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch224
2025-08-28 13:08:50.451 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 224/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 1.392e-03, size: 384, ETA: 2:08:23
2025-08-28 13:08:53.494 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 224/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.8, lr: 1.391e-03, size: 256, ETA: 2:08:20
2025-08-28 13:08:56.404 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 224/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.7, lr: 1.390e-03, size: 352, ETA: 2:08:16
2025-08-28 13:08:59.378 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 224/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 1.0, lr: 1.389e-03, size: 288, ETA: 2:08:12
2025-08-28 13:09:02.393 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 224/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 1.389e-03, size: 416, ETA: 2:08:09
2025-08-28 13:09:05.488 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 224/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 1.0, lr: 1.388e-03, size: 256, ETA: 2:08:06
2025-08-28 13:09:06.786 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:09:12.967 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:09:13.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:09:14.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5367
2025-08-28 13:09:14.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4968
2025-08-28 13:09:14.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3732
2025-08-28 13:09:14.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4689
2025-08-28 13:09:14.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:09:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:09:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-08-28 13:09:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-08-28 13:09:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.373
2025-08-28 13:09:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.469
2025-08-28 13:09:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:09:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:09:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:09:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:09:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:09:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:09:14.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:09:14.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:09:14.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:09:15.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:09:15.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:09:16.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:09:16.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:09:17.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:09:18.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:09:18.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:09:19.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:09:19.994 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:09:19.994 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:09:19.994 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:09:19.995 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:09:20.001 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.94 ms, Average inference time: 7.14 ms

2025-08-28 13:09:20.003 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:09:20.078 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:09:20.202 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch225
2025-08-28 13:09:23.016 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 225/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.7, lr: 1.387e-03, size: 256, ETA: 2:08:00
2025-08-28 13:09:26.307 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 225/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.8, lr: 1.386e-03, size: 448, ETA: 2:07:57
2025-08-28 13:09:29.368 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 225/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 9.3, iou_loss: 3.4, l1_loss: 1.3, conf_loss: 3.8, cls_loss: 0.8, lr: 1.385e-03, size: 352, ETA: 2:07:54
2025-08-28 13:09:32.414 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 225/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 1.385e-03, size: 288, ETA: 2:07:50
2025-08-28 13:09:35.439 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 225/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.384e-03, size: 448, ETA: 2:07:47
2025-08-28 13:09:38.500 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 225/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.0, cls_loss: 0.7, lr: 1.383e-03, size: 384, ETA: 2:07:43
2025-08-28 13:09:39.903 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:09:46.064 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:09:47.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:09:47.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5555
2025-08-28 13:09:47.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4880
2025-08-28 13:09:47.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3591
2025-08-28 13:09:47.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4675
2025-08-28 13:09:47.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:09:47.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:09:47.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.555
2025-08-28 13:09:47.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-08-28 13:09:47.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-28 13:09:47.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.468
2025-08-28 13:09:47.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:09:47.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:09:47.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:09:47.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:09:47.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:09:47.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:09:47.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:09:47.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:09:47.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:09:48.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:09:49.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:09:50.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:09:51.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:09:52.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:09:53.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:09:53.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:09:54.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:09:55.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:09:55.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:09:55.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:09:55.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:09:55.534 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.90 ms, Average inference time: 7.09 ms

2025-08-28 13:09:55.535 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:09:55.617 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:09:55.698 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch226
2025-08-28 13:09:58.647 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 226/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.6, lr: 1.382e-03, size: 288, ETA: 2:07:38
2025-08-28 13:10:01.648 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 226/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 8.5, iou_loss: 3.1, l1_loss: 1.5, conf_loss: 3.1, cls_loss: 0.8, lr: 1.381e-03, size: 416, ETA: 2:07:35
2025-08-28 13:10:04.600 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 226/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.7, lr: 1.380e-03, size: 512, ETA: 2:07:31
2025-08-28 13:10:07.588 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 226/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.7, lr: 1.380e-03, size: 352, ETA: 2:07:27
2025-08-28 13:10:10.601 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 226/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.7, lr: 1.379e-03, size: 480, ETA: 2:07:24
2025-08-28 13:10:13.702 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 226/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 9.3, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 4.2, cls_loss: 0.7, lr: 1.378e-03, size: 288, ETA: 2:07:21
2025-08-28 13:10:15.028 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:10:21.082 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:10:21.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:10:22.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5540
2025-08-28 13:10:22.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4736
2025-08-28 13:10:22.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3852
2025-08-28 13:10:22.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4710
2025-08-28 13:10:22.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:10:22.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:10:22.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.554
2025-08-28 13:10:22.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-28 13:10:22.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-28 13:10:22.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.471
2025-08-28 13:10:22.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:10:22.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:10:22.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:10:22.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:10:22.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:10:22.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:10:22.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:10:22.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:10:22.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:10:23.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:10:23.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:10:24.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:10:24.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:10:25.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:10:26.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:10:26.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:10:27.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:10:27.842 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:10:27.842 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:10:27.843 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:10:27.843 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:10:27.850 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.91 ms, Average inference time: 7.07 ms

2025-08-28 13:10:27.851 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:10:27.938 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:10:28.018 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch227
2025-08-28 13:10:30.923 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 227/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.8, lr: 1.377e-03, size: 384, ETA: 2:07:15
2025-08-28 13:10:33.958 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 227/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 9.8, iou_loss: 3.2, l1_loss: 1.5, conf_loss: 4.1, cls_loss: 0.9, lr: 1.376e-03, size: 256, ETA: 2:07:12
2025-08-28 13:10:37.001 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 227/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 1.376e-03, size: 320, ETA: 2:07:08
2025-08-28 13:10:40.037 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 227/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.6, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.8, cls_loss: 0.9, lr: 1.375e-03, size: 352, ETA: 2:07:05
2025-08-28 13:10:43.130 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 227/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.5, lr: 1.374e-03, size: 256, ETA: 2:07:02
2025-08-28 13:10:46.155 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 227/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 8.1, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 1.1, lr: 1.373e-03, size: 480, ETA: 2:06:58
2025-08-28 13:10:47.464 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:10:53.716 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:10:54.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:10:54.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5616
2025-08-28 13:10:55.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4968
2025-08-28 13:10:55.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3645
2025-08-28 13:10:55.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4743
2025-08-28 13:10:55.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:10:55.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:10:55.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.562
2025-08-28 13:10:55.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-08-28 13:10:55.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-08-28 13:10:55.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.474
2025-08-28 13:10:55.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:10:55.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:10:55.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:10:55.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:10:55.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:10:55.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:10:55.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:10:55.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:10:55.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:10:55.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:10:56.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:10:56.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:10:57.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:10:58.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:10:58.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:10:59.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:11:00.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:11:00.663 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:11:00.663 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:11:00.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:11:00.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:11:00.671 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.90 ms, Average inference time: 7.13 ms

2025-08-28 13:11:00.672 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:11:00.751 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:11:00.832 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch228
2025-08-28 13:11:03.655 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 228/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.372e-03, size: 512, ETA: 2:06:53
2025-08-28 13:11:06.673 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 228/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.371e-03, size: 256, ETA: 2:06:49
2025-08-28 13:11:09.783 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 228/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.8, lr: 1.371e-03, size: 576, ETA: 2:06:46
2025-08-28 13:11:12.839 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 228/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 1.370e-03, size: 352, ETA: 2:06:42
2025-08-28 13:11:15.803 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 228/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.369e-03, size: 256, ETA: 2:06:39
2025-08-28 13:11:18.783 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 228/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.368e-03, size: 448, ETA: 2:06:35
2025-08-28 13:11:20.188 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:11:26.360 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:11:27.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:11:27.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5788
2025-08-28 13:11:28.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5124
2025-08-28 13:11:28.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3596
2025-08-28 13:11:28.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4836
2025-08-28 13:11:28.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:11:28.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:11:28.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-08-28 13:11:28.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-08-28 13:11:28.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.360
2025-08-28 13:11:28.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-08-28 13:11:28.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:11:28.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:11:28.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:11:28.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:11:28.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:11:28.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:11:28.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:11:28.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:11:28.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:11:28.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:11:29.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:11:30.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:11:31.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:11:31.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:11:32.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:11:33.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:11:34.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:11:35.019 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:11:35.020 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:11:35.020 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:11:35.020 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:11:35.028 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.94 ms, Average inference time: 7.17 ms

2025-08-28 13:11:35.029 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:11:35.168 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:11:35.241 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch229
2025-08-28 13:11:38.029 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 229/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.137s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 1.367e-03, size: 480, ETA: 2:06:30
2025-08-28 13:11:41.038 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 229/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.367e-03, size: 480, ETA: 2:06:26
2025-08-28 13:11:44.020 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 229/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.8, lr: 1.366e-03, size: 576, ETA: 2:06:23
2025-08-28 13:11:47.086 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 229/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 1.365e-03, size: 288, ETA: 2:06:19
2025-08-28 13:11:50.099 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 229/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 3.8, cls_loss: 0.7, lr: 1.364e-03, size: 352, ETA: 2:06:16
2025-08-28 13:11:53.215 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 229/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.364e-03, size: 352, ETA: 2:06:13
2025-08-28 13:11:54.563 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:12:00.636 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:12:01.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:12:02.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5471
2025-08-28 13:12:02.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4690
2025-08-28 13:12:02.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3491
2025-08-28 13:12:02.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4550
2025-08-28 13:12:02.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:12:02.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:12:02.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.547
2025-08-28 13:12:02.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-08-28 13:12:02.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.349
2025-08-28 13:12:02.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.455
2025-08-28 13:12:02.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:12:02.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:12:02.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:12:02.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:12:02.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:12:02.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:12:02.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:12:02.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:12:02.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:12:03.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:12:03.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:12:04.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:12:05.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:12:06.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:12:06.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:12:07.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:12:08.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:12:09.095 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:12:09.095 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:12:09.095 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:12:09.095 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:12:09.102 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.97 ms, Average inference time: 7.16 ms

2025-08-28 13:12:09.103 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:12:09.192 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:12:09.270 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch230
2025-08-28 13:12:12.063 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 230/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.137s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.8, lr: 1.362e-03, size: 448, ETA: 2:06:07
2025-08-28 13:12:15.135 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 230/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 1.0, lr: 1.362e-03, size: 256, ETA: 2:06:04
2025-08-28 13:12:18.032 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 230/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.6, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.361e-03, size: 480, ETA: 2:06:00
2025-08-28 13:12:21.065 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 230/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 8.6, iou_loss: 3.1, l1_loss: 1.7, conf_loss: 3.0, cls_loss: 0.8, lr: 1.360e-03, size: 544, ETA: 2:05:57
2025-08-28 13:12:24.139 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 230/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.4, conf_loss: 2.1, cls_loss: 0.7, lr: 1.359e-03, size: 384, ETA: 2:05:53
2025-08-28 13:12:27.196 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 230/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 1.359e-03, size: 512, ETA: 2:05:50
2025-08-28 13:12:28.579 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:12:34.730 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:12:35.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:12:35.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5851
2025-08-28 13:12:36.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5091
2025-08-28 13:12:36.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3806
2025-08-28 13:12:36.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4916
2025-08-28 13:12:36.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:12:36.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:12:36.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.585
2025-08-28 13:12:36.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-08-28 13:12:36.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-08-28 13:12:36.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-08-28 13:12:36.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:12:36.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:12:36.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:12:36.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:12:36.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:12:36.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:12:36.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:12:36.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:12:36.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:12:36.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:12:37.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:12:38.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:12:38.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:12:39.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:12:39.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:12:40.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:12:41.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:12:41.778 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:12:41.778 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:12:41.778 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:12:41.778 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:12:41.785 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.94 ms, Average inference time: 7.15 ms

2025-08-28 13:12:41.787 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:12:41.917 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:12:41.988 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch231
2025-08-28 13:12:44.773 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 231/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.138s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.358e-03, size: 256, ETA: 2:05:44
2025-08-28 13:12:47.745 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 231/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 1.357e-03, size: 480, ETA: 2:05:41
2025-08-28 13:12:50.781 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 231/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.8, lr: 1.356e-03, size: 256, ETA: 2:05:38
2025-08-28 13:12:53.840 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 231/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.8, lr: 1.355e-03, size: 256, ETA: 2:05:34
2025-08-28 13:12:56.850 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 231/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 1.355e-03, size: 512, ETA: 2:05:31
2025-08-28 13:12:59.893 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 231/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 1.9, cls_loss: 0.6, lr: 1.354e-03, size: 544, ETA: 2:05:27
2025-08-28 13:13:01.219 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:13:07.534 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:13:09.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:13:10.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5566
2025-08-28 13:13:10.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4816
2025-08-28 13:13:10.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3592
2025-08-28 13:13:10.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4658
2025-08-28 13:13:10.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:13:10.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:13:10.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.557
2025-08-28 13:13:10.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-28 13:13:10.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-28 13:13:10.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.466
2025-08-28 13:13:10.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:13:10.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:13:10.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:13:10.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:13:10.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:13:10.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:13:10.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:13:10.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:13:10.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:13:12.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:13:13.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:13:15.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:13:16.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:13:17.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:13:19.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:13:20.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:13:22.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:13:23.682 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:13:23.683 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:13:23.683 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:13:23.683 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:13:23.698 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.99 ms, Average inference time: 7.25 ms

2025-08-28 13:13:23.699 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:13:23.833 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:13:23.908 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch232
2025-08-28 13:13:26.709 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 232/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.138s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.7, lr: 1.353e-03, size: 384, ETA: 2:05:22
2025-08-28 13:13:29.725 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 232/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 0.7, lr: 1.352e-03, size: 512, ETA: 2:05:18
2025-08-28 13:13:32.807 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 232/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.6, lr: 1.351e-03, size: 576, ETA: 2:05:15
2025-08-28 13:13:35.848 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 232/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 1.350e-03, size: 256, ETA: 2:05:12
2025-08-28 13:13:38.986 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 232/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 1.7, cls_loss: 0.6, lr: 1.350e-03, size: 384, ETA: 2:05:08
2025-08-28 13:13:41.977 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 232/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 0.7, lr: 1.349e-03, size: 512, ETA: 2:05:05
2025-08-28 13:13:43.374 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:13:49.679 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:13:50.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:13:51.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5696
2025-08-28 13:13:51.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5030
2025-08-28 13:13:51.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3210
2025-08-28 13:13:51.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4645
2025-08-28 13:13:51.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:13:51.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:13:51.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.570
2025-08-28 13:13:51.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-08-28 13:13:51.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.321
2025-08-28 13:13:51.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.465
2025-08-28 13:13:51.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:13:51.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:13:51.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:13:51.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:13:51.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:13:51.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:13:51.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:13:51.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:13:51.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:13:52.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:13:52.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:13:53.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:13:54.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:13:55.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:13:55.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:13:56.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:13:57.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:13:58.125 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:13:58.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:13:58.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:13:58.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:13:58.133 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.94 ms, Average inference time: 7.16 ms

2025-08-28 13:13:58.135 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:13:58.212 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:13:58.291 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch233
2025-08-28 13:14:01.208 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 233/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 3.3, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.4, lr: 1.348e-03, size: 384, ETA: 2:05:00
2025-08-28 13:14:04.184 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 233/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 1.5, lr: 1.347e-03, size: 576, ETA: 2:04:56
2025-08-28 13:14:07.220 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 233/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.346e-03, size: 416, ETA: 2:04:53
2025-08-28 13:14:10.231 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 233/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 1.346e-03, size: 256, ETA: 2:04:49
2025-08-28 13:14:13.116 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 233/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 1.345e-03, size: 256, ETA: 2:04:46
2025-08-28 13:14:16.305 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 233/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.344e-03, size: 288, ETA: 2:04:42
2025-08-28 13:14:17.631 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:14:23.964 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:14:25.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:14:26.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5682
2025-08-28 13:14:26.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4840
2025-08-28 13:14:26.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3272
2025-08-28 13:14:26.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4598
2025-08-28 13:14:26.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:14:26.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:14:26.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.568
2025-08-28 13:14:26.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-28 13:14:26.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.327
2025-08-28 13:14:26.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.460
2025-08-28 13:14:26.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:14:26.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:14:26.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:14:26.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:14:26.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:14:26.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:14:26.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:14:26.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:14:26.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:14:27.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:14:28.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:14:29.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:14:30.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:14:32.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:14:33.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:14:34.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:14:35.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:14:36.538 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:14:36.538 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:14:36.538 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:14:36.539 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:14:36.546 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.95 ms, Average inference time: 7.14 ms

2025-08-28 13:14:36.548 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:14:36.633 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:14:36.713 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch234
2025-08-28 13:14:39.636 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 234/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.6, lr: 1.343e-03, size: 320, ETA: 2:04:37
2025-08-28 13:14:42.629 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 234/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.342e-03, size: 448, ETA: 2:04:34
2025-08-28 13:14:45.655 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 234/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.341e-03, size: 544, ETA: 2:04:30
2025-08-28 13:14:48.654 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 234/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.8, lr: 1.341e-03, size: 416, ETA: 2:04:27
2025-08-28 13:14:51.619 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 234/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.6, lr: 1.340e-03, size: 480, ETA: 2:04:23
2025-08-28 13:14:54.611 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 234/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 2.9, cls_loss: 0.7, lr: 1.339e-03, size: 512, ETA: 2:04:20
2025-08-28 13:14:55.972 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:15:02.232 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:15:03.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:15:03.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5711
2025-08-28 13:15:03.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5114
2025-08-28 13:15:03.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3645
2025-08-28 13:15:03.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4823
2025-08-28 13:15:03.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:15:03.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:15:03.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.571
2025-08-28 13:15:03.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-08-28 13:15:03.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-08-28 13:15:03.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.482
2025-08-28 13:15:03.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:15:03.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:15:03.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:15:03.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:15:03.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:15:03.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:15:03.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:15:03.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:15:03.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:15:04.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:15:05.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:15:06.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:15:06.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:15:07.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:15:08.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:15:09.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:15:09.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:15:10.521 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:15:10.521 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:15:10.521 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:15:10.521 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:15:10.528 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.93 ms, Average inference time: 7.11 ms

2025-08-28 13:15:10.530 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:15:10.612 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:15:10.690 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch235
2025-08-28 13:15:13.519 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 235/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.7, lr: 1.338e-03, size: 480, ETA: 2:04:14
2025-08-28 13:15:16.581 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 235/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.337e-03, size: 480, ETA: 2:04:11
2025-08-28 13:15:19.660 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 235/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.6, lr: 1.336e-03, size: 480, ETA: 2:04:08
2025-08-28 13:15:22.687 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 235/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.5, lr: 1.336e-03, size: 384, ETA: 2:04:04
2025-08-28 13:15:25.674 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 235/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.335e-03, size: 576, ETA: 2:04:01
2025-08-28 13:15:28.851 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 235/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 8.3, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 1.0, lr: 1.334e-03, size: 576, ETA: 2:03:58
2025-08-28 13:15:30.288 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:15:36.577 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:15:37.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:15:37.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5631
2025-08-28 13:15:37.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4637
2025-08-28 13:15:37.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3662
2025-08-28 13:15:37.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4644
2025-08-28 13:15:37.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:15:37.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:15:37.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.563
2025-08-28 13:15:37.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.464
2025-08-28 13:15:37.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-08-28 13:15:37.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.464
2025-08-28 13:15:37.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:15:37.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:15:37.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:15:37.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:15:37.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:15:37.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:15:37.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:15:37.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:15:37.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:15:38.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:15:38.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:15:39.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:15:39.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:15:40.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:15:40.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:15:41.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:15:41.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:15:42.028 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:15:42.028 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:15:42.028 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:15:42.028 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:15:42.035 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.88 ms, Average inference time: 7.15 ms

2025-08-28 13:15:42.038 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:15:42.153 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:15:42.227 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch236
2025-08-28 13:15:45.165 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 236/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.333e-03, size: 320, ETA: 2:03:53
2025-08-28 13:15:48.252 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 236/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 1.332e-03, size: 288, ETA: 2:03:49
2025-08-28 13:15:51.221 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 236/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 3.2, cls_loss: 0.6, lr: 1.332e-03, size: 512, ETA: 2:03:46
2025-08-28 13:15:54.152 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 236/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.331e-03, size: 288, ETA: 2:03:42
2025-08-28 13:15:57.156 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 236/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.6, lr: 1.330e-03, size: 576, ETA: 2:03:39
2025-08-28 13:16:00.318 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 236/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.9, lr: 1.329e-03, size: 512, ETA: 2:03:35
2025-08-28 13:16:01.711 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:16:08.061 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:16:08.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:16:09.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5875
2025-08-28 13:16:09.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5103
2025-08-28 13:16:09.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3362
2025-08-28 13:16:09.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4780
2025-08-28 13:16:09.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:16:09.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:16:09.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-08-28 13:16:09.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.510
2025-08-28 13:16:09.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.336
2025-08-28 13:16:09.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-08-28 13:16:09.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:16:09.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:16:09.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:16:09.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:16:09.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:16:09.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:16:09.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:16:09.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:16:09.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:16:10.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:16:11.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:16:11.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:16:12.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:16:13.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:16:13.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:16:14.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:16:15.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:16:15.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:16:15.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:16:15.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:16:15.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:16:15.986 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.94 ms, Average inference time: 7.21 ms

2025-08-28 13:16:15.987 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:16:16.110 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:16:16.206 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch237
2025-08-28 13:16:19.060 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 237/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 8.6, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.4, cls_loss: 0.9, lr: 1.328e-03, size: 256, ETA: 2:03:30
2025-08-28 13:16:21.989 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 237/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 1.327e-03, size: 320, ETA: 2:03:27
2025-08-28 13:16:24.976 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 237/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.327e-03, size: 384, ETA: 2:03:23
2025-08-28 13:16:27.896 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 237/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 1.0, lr: 1.326e-03, size: 544, ETA: 2:03:20
2025-08-28 13:16:30.974 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 237/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.8, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 3.7, cls_loss: 0.8, lr: 1.325e-03, size: 512, ETA: 2:03:16
2025-08-28 13:16:33.991 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 237/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.8, lr: 1.324e-03, size: 384, ETA: 2:03:13
2025-08-28 13:16:35.358 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:16:41.543 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:16:42.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:16:43.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5311
2025-08-28 13:16:43.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4467
2025-08-28 13:16:43.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3631
2025-08-28 13:16:43.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4470
2025-08-28 13:16:43.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:16:43.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:16:43.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-08-28 13:16:43.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-08-28 13:16:43.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-08-28 13:16:43.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.447
2025-08-28 13:16:43.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:16:43.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:16:43.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:16:43.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:16:43.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:16:43.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:16:43.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:16:43.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:16:43.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:16:44.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:16:45.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:16:46.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:16:47.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:16:48.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:16:49.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:16:50.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:16:51.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:16:52.528 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:16:52.528 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 13:16:52.528 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 13:16:52.528 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:16:52.536 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.94 ms, Average inference time: 7.10 ms

2025-08-28 13:16:52.537 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:16:52.615 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:16:52.697 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch238
2025-08-28 13:16:55.477 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 238/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.138s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 1.323e-03, size: 448, ETA: 2:03:07
2025-08-28 13:16:58.546 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 238/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.322e-03, size: 448, ETA: 2:03:04
2025-08-28 13:17:01.722 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 238/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 1.322e-03, size: 384, ETA: 2:03:01
2025-08-28 13:17:04.717 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 238/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.321e-03, size: 384, ETA: 2:02:57
2025-08-28 13:17:07.779 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 238/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 8.6, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 3.4, cls_loss: 0.7, lr: 1.320e-03, size: 288, ETA: 2:02:54
2025-08-28 13:17:10.791 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 238/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.005s, total_loss: 10.2, iou_loss: 2.9, l1_loss: 1.5, conf_loss: 5.0, cls_loss: 0.8, lr: 1.319e-03, size: 384, ETA: 2:02:51
2025-08-28 13:17:12.238 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:17:18.329 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:17:18.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:17:19.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5598
2025-08-28 13:17:19.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4735
2025-08-28 13:17:19.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3424
2025-08-28 13:17:19.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4586
2025-08-28 13:17:19.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:17:19.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:17:19.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.560
2025-08-28 13:17:19.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-28 13:17:19.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.342
2025-08-28 13:17:19.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.459
2025-08-28 13:17:19.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:17:19.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:17:19.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:17:19.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:17:19.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:17:19.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:17:19.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:17:19.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:17:19.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:17:20.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:17:20.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:17:21.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:17:21.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:17:22.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:17:22.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:17:23.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:17:23.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:17:24.148 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:17:24.148 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:17:24.148 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:17:24.148 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:17:24.155 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.94 ms, Average inference time: 7.04 ms

2025-08-28 13:17:24.156 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:17:24.238 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:17:24.316 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch239
2025-08-28 13:17:27.349 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 239/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 1.318e-03, size: 320, ETA: 2:02:46
2025-08-28 13:17:30.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 239/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.9, lr: 1.317e-03, size: 288, ETA: 2:02:42
2025-08-28 13:17:33.369 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 239/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.8, lr: 1.317e-03, size: 512, ETA: 2:02:39
2025-08-28 13:17:36.425 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 239/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 1.316e-03, size: 416, ETA: 2:02:35
2025-08-28 13:17:39.429 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 239/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.315e-03, size: 544, ETA: 2:02:32
2025-08-28 13:17:42.434 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 239/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 1.314e-03, size: 448, ETA: 2:02:29
2025-08-28 13:17:43.824 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:17:50.050 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:17:50.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:17:51.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5727
2025-08-28 13:17:51.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5035
2025-08-28 13:17:51.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3587
2025-08-28 13:17:51.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4783
2025-08-28 13:17:51.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:17:51.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:17:51.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.573
2025-08-28 13:17:51.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-08-28 13:17:51.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-28 13:17:51.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-08-28 13:17:51.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:17:51.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:17:51.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:17:51.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:17:51.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:17:51.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:17:51.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:17:51.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:17:51.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:17:52.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:17:52.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:17:53.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:17:54.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:17:54.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:17:55.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:17:56.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:17:56.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:17:57.556 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:17:57.556 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:17:57.556 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:17:57.556 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:17:57.563 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.91 ms, Average inference time: 7.16 ms

2025-08-28 13:17:57.564 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:17:57.646 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:17:57.727 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch240
2025-08-28 13:18:00.513 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 240/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.137s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.313e-03, size: 416, ETA: 2:02:23
2025-08-28 13:18:03.470 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 240/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.5, cls_loss: 0.5, lr: 1.312e-03, size: 320, ETA: 2:02:20
2025-08-28 13:18:06.460 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 240/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.312e-03, size: 576, ETA: 2:02:16
2025-08-28 13:18:09.591 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 240/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.311e-03, size: 352, ETA: 2:02:13
2025-08-28 13:18:12.679 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 240/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 1.310e-03, size: 416, ETA: 2:02:10
2025-08-28 13:18:15.680 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 240/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.8, lr: 1.309e-03, size: 416, ETA: 2:02:06
2025-08-28 13:18:16.999 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:18:23.216 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:18:24.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:18:24.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5838
2025-08-28 13:18:25.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4896
2025-08-28 13:18:25.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3842
2025-08-28 13:18:25.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4858
2025-08-28 13:18:25.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:18:25.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:18:25.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-08-28 13:18:25.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-28 13:18:25.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-08-28 13:18:25.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.486
2025-08-28 13:18:25.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:18:25.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:18:25.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:18:25.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:18:25.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:18:25.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:18:25.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:18:25.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:18:25.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:18:25.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:18:26.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:18:27.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:18:28.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:18:29.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:18:30.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:18:30.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:18:31.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:18:32.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:18:32.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:18:32.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:18:32.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:18:32.639 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.93 ms, Average inference time: 7.16 ms

2025-08-28 13:18:32.640 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:18:32.724 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:18:32.846 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch241
2025-08-28 13:18:35.850 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 241/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.6, lr: 1.308e-03, size: 512, ETA: 2:02:01
2025-08-28 13:18:38.860 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 241/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 1.307e-03, size: 480, ETA: 2:01:58
2025-08-28 13:18:41.976 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 241/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.9, lr: 1.307e-03, size: 480, ETA: 2:01:55
2025-08-28 13:18:45.126 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 241/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.306e-03, size: 352, ETA: 2:01:51
2025-08-28 13:18:48.179 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 241/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.8, lr: 1.305e-03, size: 544, ETA: 2:01:48
2025-08-28 13:18:51.347 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 241/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.006s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.8, lr: 1.304e-03, size: 320, ETA: 2:01:45
2025-08-28 13:18:52.696 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:18:58.821 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:18:59.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:19:00.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5699
2025-08-28 13:19:00.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5110
2025-08-28 13:19:00.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3231
2025-08-28 13:19:00.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4680
2025-08-28 13:19:00.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:19:00.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:19:00.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.570
2025-08-28 13:19:00.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-08-28 13:19:00.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.323
2025-08-28 13:19:00.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.468
2025-08-28 13:19:00.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:19:00.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:19:00.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:19:00.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:19:00.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:19:00.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:19:00.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:19:00.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:19:00.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:19:01.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:19:02.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:19:03.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:19:04.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:19:05.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:19:06.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:19:07.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:19:08.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:19:09.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:19:09.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:19:09.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:19:09.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:19:09.060 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.92 ms, Average inference time: 7.06 ms

2025-08-28 13:19:09.062 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:19:09.149 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:19:09.230 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch242
2025-08-28 13:19:12.210 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 242/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.6, conf_loss: 2.8, cls_loss: 0.7, lr: 1.303e-03, size: 544, ETA: 2:01:40
2025-08-28 13:19:15.377 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 242/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.8, lr: 1.302e-03, size: 384, ETA: 2:01:37
2025-08-28 13:19:18.509 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 242/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.302e-03, size: 576, ETA: 2:01:33
2025-08-28 13:19:21.577 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 242/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.8, lr: 1.301e-03, size: 384, ETA: 2:01:30
2025-08-28 13:19:24.703 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 242/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.300e-03, size: 288, ETA: 2:01:27
2025-08-28 13:19:27.772 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 242/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.6, lr: 1.299e-03, size: 544, ETA: 2:01:23
2025-08-28 13:19:29.149 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:19:35.416 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:19:36.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:19:36.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5571
2025-08-28 13:19:36.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5116
2025-08-28 13:19:36.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3452
2025-08-28 13:19:36.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4713
2025-08-28 13:19:36.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:19:36.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:19:36.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.557
2025-08-28 13:19:36.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-08-28 13:19:36.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.345
2025-08-28 13:19:36.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.471
2025-08-28 13:19:36.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:19:36.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:19:36.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:19:36.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:19:36.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:19:36.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:19:36.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:19:36.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:19:36.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:19:37.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:19:37.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:19:38.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:19:38.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:19:39.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:19:39.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:19:40.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:19:40.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:19:41.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:19:41.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:19:41.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:19:41.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:19:41.407 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.34 ms, Average NMS time: 0.90 ms, Average inference time: 7.24 ms

2025-08-28 13:19:41.408 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:19:41.487 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:19:41.569 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch243
2025-08-28 13:19:44.422 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 243/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 1.298e-03, size: 448, ETA: 2:01:18
2025-08-28 13:19:47.354 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 243/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 4.1, cls_loss: 0.8, lr: 1.297e-03, size: 416, ETA: 2:01:15
2025-08-28 13:19:50.341 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 243/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 1.297e-03, size: 448, ETA: 2:01:11
2025-08-28 13:19:53.226 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 243/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.296e-03, size: 256, ETA: 2:01:08
2025-08-28 13:19:56.272 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 243/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.0, cls_loss: 0.6, lr: 1.295e-03, size: 448, ETA: 2:01:04
2025-08-28 13:19:59.279 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 243/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.9, lr: 1.294e-03, size: 256, ETA: 2:01:01
2025-08-28 13:20:00.653 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:20:06.929 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:20:07.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:20:08.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5610
2025-08-28 13:20:08.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4759
2025-08-28 13:20:08.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3535
2025-08-28 13:20:08.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4634
2025-08-28 13:20:08.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:20:08.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:20:08.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.561
2025-08-28 13:20:08.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-08-28 13:20:08.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.353
2025-08-28 13:20:08.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.463
2025-08-28 13:20:08.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:20:08.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:20:08.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:20:08.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:20:08.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:20:08.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:20:08.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:20:08.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:20:08.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:20:09.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:20:10.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:20:11.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:20:12.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:20:12.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:20:13.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:20:14.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:20:15.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:20:16.138 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:20:16.139 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:20:16.139 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:20:16.139 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:20:16.146 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.95 ms, Average inference time: 7.21 ms

2025-08-28 13:20:16.147 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:20:16.238 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:20:16.319 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch244
2025-08-28 13:20:19.165 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 244/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.293e-03, size: 544, ETA: 2:00:56
2025-08-28 13:20:22.309 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 244/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.292e-03, size: 320, ETA: 2:00:52
2025-08-28 13:20:25.511 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 244/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 1.292e-03, size: 416, ETA: 2:00:49
2025-08-28 13:20:28.454 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 244/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 1.291e-03, size: 320, ETA: 2:00:46
2025-08-28 13:20:31.545 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 244/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.7, lr: 1.290e-03, size: 576, ETA: 2:00:42
2025-08-28 13:20:34.677 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 244/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.005s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 1.289e-03, size: 512, ETA: 2:00:39
2025-08-28 13:20:35.997 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:20:42.087 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:20:42.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:20:43.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5494
2025-08-28 13:20:43.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5060
2025-08-28 13:20:43.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3459
2025-08-28 13:20:43.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4671
2025-08-28 13:20:43.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:20:43.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:20:43.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.549
2025-08-28 13:20:43.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-08-28 13:20:43.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.346
2025-08-28 13:20:43.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.467
2025-08-28 13:20:43.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:20:43.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:20:43.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:20:43.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:20:43.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:20:43.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:20:43.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:20:43.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:20:43.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:20:44.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:20:44.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:20:45.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:20:45.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:20:46.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:20:47.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:20:47.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:20:48.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:20:48.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:20:48.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:20:48.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:20:48.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:20:48.893 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.94 ms, Average inference time: 7.20 ms

2025-08-28 13:20:48.894 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:20:48.996 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:20:49.073 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch245
2025-08-28 13:20:51.931 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 245/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 8.8, iou_loss: 3.2, l1_loss: 1.5, conf_loss: 3.2, cls_loss: 0.9, lr: 1.288e-03, size: 448, ETA: 2:00:34
2025-08-28 13:20:55.014 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 245/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.8, lr: 1.287e-03, size: 288, ETA: 2:00:30
2025-08-28 13:20:58.081 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 245/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 1.287e-03, size: 416, ETA: 2:00:27
2025-08-28 13:21:01.070 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 245/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 1.286e-03, size: 448, ETA: 2:00:24
2025-08-28 13:21:04.052 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 245/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 1.285e-03, size: 512, ETA: 2:00:20
2025-08-28 13:21:07.104 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 245/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 1.284e-03, size: 352, ETA: 2:00:17
2025-08-28 13:21:08.494 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:21:14.658 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:21:15.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:21:16.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5732
2025-08-28 13:21:16.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4751
2025-08-28 13:21:16.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3325
2025-08-28 13:21:16.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4603
2025-08-28 13:21:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:21:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:21:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.573
2025-08-28 13:21:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-08-28 13:21:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.332
2025-08-28 13:21:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.460
2025-08-28 13:21:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:21:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:21:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:21:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:21:16.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:21:16.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:21:16.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:21:16.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:21:16.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:21:17.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:21:18.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:21:19.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:21:20.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:21:22.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:21:22.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:21:24.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:21:25.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:21:26.090 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:21:26.090 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:21:26.090 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:21:26.090 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:21:26.098 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.96 ms, Average inference time: 7.20 ms

2025-08-28 13:21:26.099 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:21:26.247 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:21:26.322 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch246
2025-08-28 13:21:29.214 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 246/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 1.283e-03, size: 416, ETA: 2:00:12
2025-08-28 13:21:32.364 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 246/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.282e-03, size: 576, ETA: 2:00:09
2025-08-28 13:21:35.358 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 246/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.7, lr: 1.282e-03, size: 256, ETA: 2:00:05
2025-08-28 13:21:38.390 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 246/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 1.281e-03, size: 416, ETA: 2:00:02
2025-08-28 13:21:41.416 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 246/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.6, lr: 1.280e-03, size: 384, ETA: 1:59:58
2025-08-28 13:21:44.424 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 246/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.8, lr: 1.279e-03, size: 288, ETA: 1:59:55
2025-08-28 13:21:45.759 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:21:51.981 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:21:53.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:21:53.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5775
2025-08-28 13:21:54.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5150
2025-08-28 13:21:54.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3608
2025-08-28 13:21:54.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4844
2025-08-28 13:21:54.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:21:54.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:21:54.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.577
2025-08-28 13:21:54.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 13:21:54.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-08-28 13:21:54.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-08-28 13:21:54.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:21:54.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:21:54.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:21:54.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:21:54.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:21:54.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:21:54.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:21:54.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:21:54.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:21:55.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:21:56.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:21:57.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:21:58.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:21:59.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:22:00.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:22:01.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:22:02.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:22:03.191 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:22:03.191 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:22:03.191 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:22:03.192 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:22:03.199 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.94 ms, Average inference time: 7.19 ms

2025-08-28 13:22:03.214 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:22:03.293 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:22:03.374 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch247
2025-08-28 13:22:06.239 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 247/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.6, lr: 1.278e-03, size: 544, ETA: 1:59:50
2025-08-28 13:22:09.358 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 247/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.2, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.277e-03, size: 256, ETA: 1:59:46
2025-08-28 13:22:12.274 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 247/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 4.3, cls_loss: 0.6, lr: 1.277e-03, size: 448, ETA: 1:59:43
2025-08-28 13:22:15.306 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 247/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.8, lr: 1.276e-03, size: 416, ETA: 1:59:40
2025-08-28 13:22:18.417 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 247/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 1.275e-03, size: 480, ETA: 1:59:36
2025-08-28 13:22:21.415 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 247/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.274e-03, size: 448, ETA: 1:59:33
2025-08-28 13:22:22.774 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:22:28.914 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:22:29.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:22:30.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5607
2025-08-28 13:22:30.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5034
2025-08-28 13:22:30.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3554
2025-08-28 13:22:30.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4732
2025-08-28 13:22:30.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:22:30.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:22:30.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.561
2025-08-28 13:22:30.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-08-28 13:22:30.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.355
2025-08-28 13:22:30.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.473
2025-08-28 13:22:30.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:22:30.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:22:30.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:22:30.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:22:30.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:22:30.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:22:30.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:22:30.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:22:30.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:22:31.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:22:32.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:22:33.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:22:34.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:22:34.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:22:35.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:22:36.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:22:37.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:22:38.200 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:22:38.200 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:22:38.200 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:22:38.200 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:22:38.208 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.93 ms, Average inference time: 7.15 ms

2025-08-28 13:22:38.209 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:22:38.333 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:22:38.407 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch248
2025-08-28 13:22:41.283 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 248/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.6, lr: 1.273e-03, size: 576, ETA: 1:59:28
2025-08-28 13:22:44.326 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 248/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 1.272e-03, size: 416, ETA: 1:59:24
2025-08-28 13:22:47.216 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 248/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.272e-03, size: 384, ETA: 1:59:21
2025-08-28 13:22:50.288 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 248/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 3.2, cls_loss: 0.7, lr: 1.271e-03, size: 256, ETA: 1:59:17
2025-08-28 13:22:53.354 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 248/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 1.270e-03, size: 352, ETA: 1:59:14
2025-08-28 13:22:56.326 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 248/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.269e-03, size: 448, ETA: 1:59:11
2025-08-28 13:22:57.670 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:23:03.711 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:23:04.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:23:05.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5751
2025-08-28 13:23:05.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5153
2025-08-28 13:23:05.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3555
2025-08-28 13:23:05.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4820
2025-08-28 13:23:05.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:23:05.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:23:05.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.575
2025-08-28 13:23:05.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 13:23:05.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-08-28 13:23:05.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.482
2025-08-28 13:23:05.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:23:05.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:23:05.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:23:05.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:23:05.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:23:05.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:23:05.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:23:05.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:23:05.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:23:05.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:23:06.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:23:07.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:23:08.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:23:08.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:23:09.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:23:10.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:23:10.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:23:11.614 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:23:11.614 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:23:11.615 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:23:11.615 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:23:11.622 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.92 ms, Average inference time: 7.19 ms

2025-08-28 13:23:11.623 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:23:11.710 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:23:11.791 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch249
2025-08-28 13:23:14.621 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 249/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.0, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.6, lr: 1.268e-03, size: 480, ETA: 1:59:05
2025-08-28 13:23:17.801 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 249/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 8.7, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 3.7, cls_loss: 1.0, lr: 1.267e-03, size: 544, ETA: 1:59:02
2025-08-28 13:23:20.858 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 249/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 1.267e-03, size: 576, ETA: 1:58:59
2025-08-28 13:23:24.054 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 249/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 1.266e-03, size: 288, ETA: 1:58:56
2025-08-28 13:23:27.009 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 249/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.6, lr: 1.265e-03, size: 576, ETA: 1:58:52
2025-08-28 13:23:30.099 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 249/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.8, lr: 1.264e-03, size: 352, ETA: 1:58:49
2025-08-28 13:23:31.499 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:23:37.787 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:23:39.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:23:39.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5793
2025-08-28 13:23:39.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4669
2025-08-28 13:23:40.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3750
2025-08-28 13:23:40.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4737
2025-08-28 13:23:40.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:23:40.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:23:40.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-08-28 13:23:40.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.467
2025-08-28 13:23:40.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-08-28 13:23:40.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.474
2025-08-28 13:23:40.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:23:40.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:23:40.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:23:40.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:23:40.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:23:40.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:23:40.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:23:40.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:23:40.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:23:41.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:23:42.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:23:43.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:23:44.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:23:45.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:23:46.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:23:47.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:23:48.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:23:49.193 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:23:49.193 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:23:49.193 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:23:49.193 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:23:49.201 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.99 ms, Average inference time: 7.14 ms

2025-08-28 13:23:49.202 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:23:49.283 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:23:49.407 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch250
2025-08-28 13:23:52.276 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 250/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.8, lr: 1.263e-03, size: 448, ETA: 1:58:44
2025-08-28 13:23:55.327 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 250/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.8, lr: 1.262e-03, size: 544, ETA: 1:58:40
2025-08-28 13:23:58.355 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 250/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.262e-03, size: 512, ETA: 1:58:37
2025-08-28 13:24:01.438 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 250/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.8, lr: 1.261e-03, size: 352, ETA: 1:58:34
2025-08-28 13:24:04.432 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 250/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.260e-03, size: 544, ETA: 1:58:30
2025-08-28 13:24:07.457 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 250/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 1.4, cls_loss: 0.5, lr: 1.259e-03, size: 384, ETA: 1:58:27
2025-08-28 13:24:08.825 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:24:14.919 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:24:15.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:24:16.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5727
2025-08-28 13:24:16.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5074
2025-08-28 13:24:16.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3821
2025-08-28 13:24:16.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4874
2025-08-28 13:24:16.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:24:16.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:24:16.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.573
2025-08-28 13:24:16.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-08-28 13:24:16.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-08-28 13:24:16.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-08-28 13:24:16.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:24:16.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:24:16.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:24:16.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:24:16.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:24:16.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:24:16.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:24:16.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:24:16.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:24:17.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:24:18.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:24:18.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:24:19.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:24:20.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:24:20.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:24:21.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:24:22.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:24:23.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:24:23.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:24:23.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:24:23.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:24:23.116 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.93 ms, Average inference time: 7.11 ms

2025-08-28 13:24:23.117 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:24:23.200 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:24:23.278 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch251
2025-08-28 13:24:25.988 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 251/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.134s, data_time: 0.002s, total_loss: 2.9, iou_loss: 1.3, l1_loss: 0.3, conf_loss: 0.9, cls_loss: 0.4, lr: 1.258e-03, size: 256, ETA: 1:58:22
2025-08-28 13:24:29.229 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 251/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 1.257e-03, size: 352, ETA: 1:58:19
2025-08-28 13:24:32.253 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 251/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 1.256e-03, size: 288, ETA: 1:58:15
2025-08-28 13:24:35.337 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 251/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.8, lr: 1.256e-03, size: 352, ETA: 1:58:12
2025-08-28 13:24:38.322 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 251/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 1.255e-03, size: 416, ETA: 1:58:08
2025-08-28 13:24:41.314 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 251/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.7, lr: 1.254e-03, size: 384, ETA: 1:58:05
2025-08-28 13:24:42.674 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:24:48.767 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:24:49.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:24:49.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5525
2025-08-28 13:24:50.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4683
2025-08-28 13:24:50.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3992
2025-08-28 13:24:50.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4733
2025-08-28 13:24:50.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:24:50.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:24:50.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.553
2025-08-28 13:24:50.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-08-28 13:24:50.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-08-28 13:24:50.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.473
2025-08-28 13:24:50.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:24:50.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:24:50.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:24:50.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:24:50.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:24:50.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:24:50.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:24:50.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:24:50.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:24:50.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:24:51.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:24:51.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:24:52.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:24:53.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:24:53.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:24:54.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:24:54.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:24:55.261 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:24:55.261 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:24:55.262 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:24:55.262 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:24:55.269 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.93 ms, Average inference time: 7.13 ms

2025-08-28 13:24:55.270 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:24:55.357 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:24:55.437 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch252
2025-08-28 13:24:58.281 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 252/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.5, lr: 1.253e-03, size: 480, ETA: 1:58:00
2025-08-28 13:25:01.363 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 252/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 0.8, cls_loss: 0.5, lr: 1.252e-03, size: 448, ETA: 1:57:57
2025-08-28 13:25:04.515 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 252/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 1.251e-03, size: 448, ETA: 1:57:53
2025-08-28 13:25:07.518 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 252/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.9, lr: 1.251e-03, size: 416, ETA: 1:57:50
2025-08-28 13:25:10.556 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 252/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.6, lr: 1.250e-03, size: 512, ETA: 1:57:47
2025-08-28 13:25:13.720 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 252/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.8, lr: 1.249e-03, size: 288, ETA: 1:57:43
2025-08-28 13:25:15.036 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:25:21.198 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:25:22.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:25:22.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5854
2025-08-28 13:25:22.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5253
2025-08-28 13:25:22.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3708
2025-08-28 13:25:22.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4938
2025-08-28 13:25:22.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:25:22.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:25:22.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.585
2025-08-28 13:25:22.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 13:25:22.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-08-28 13:25:22.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-08-28 13:25:22.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:25:22.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:25:22.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:25:22.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:25:22.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:25:22.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:25:22.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:25:22.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:25:22.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:25:23.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:25:24.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:25:24.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:25:25.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:25:26.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:25:27.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:25:27.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:25:28.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:25:29.174 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:25:29.174 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:25:29.174 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:25:29.174 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:25:29.181 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.93 ms, Average inference time: 7.16 ms

2025-08-28 13:25:29.183 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:25:29.262 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:25:29.342 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch253
2025-08-28 13:25:32.222 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 253/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.248e-03, size: 512, ETA: 1:57:38
2025-08-28 13:25:35.250 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 253/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 1.247e-03, size: 256, ETA: 1:57:35
2025-08-28 13:25:38.359 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 253/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.6, lr: 1.246e-03, size: 512, ETA: 1:57:32
2025-08-28 13:25:41.532 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 253/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.246e-03, size: 576, ETA: 1:57:28
2025-08-28 13:25:44.786 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 253/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.2, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.5, lr: 1.245e-03, size: 288, ETA: 1:57:25
2025-08-28 13:25:47.936 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 253/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.5, lr: 1.244e-03, size: 512, ETA: 1:57:22
2025-08-28 13:25:49.271 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:25:55.519 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:25:56.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:25:56.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5422
2025-08-28 13:25:56.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4682
2025-08-28 13:25:56.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3253
2025-08-28 13:25:56.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4452
2025-08-28 13:25:56.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:25:56.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:25:56.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-08-28 13:25:56.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-08-28 13:25:56.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.325
2025-08-28 13:25:56.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.445
2025-08-28 13:25:56.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:25:56.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:25:56.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:25:56.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:25:56.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:25:56.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:25:56.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:25:56.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:25:56.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:25:57.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:25:57.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:25:58.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:25:58.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:25:59.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:25:59.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:26:00.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:26:00.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:26:01.226 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:26:01.227 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 13:26:01.227 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 13:26:01.227 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:26:01.234 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.90 ms, Average inference time: 7.14 ms

2025-08-28 13:26:01.235 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:26:01.318 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:26:01.479 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch254
2025-08-28 13:26:04.413 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 254/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.8, lr: 1.243e-03, size: 448, ETA: 1:57:17
2025-08-28 13:26:07.410 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 254/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 1.242e-03, size: 256, ETA: 1:57:14
2025-08-28 13:26:10.438 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 254/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.6, lr: 1.241e-03, size: 416, ETA: 1:57:10
2025-08-28 13:26:13.402 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 254/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 9.9, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 5.2, cls_loss: 0.8, lr: 1.240e-03, size: 512, ETA: 1:57:07
2025-08-28 13:26:16.343 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 254/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 29.1, iou_loss: 4.9, l1_loss: 3.2, conf_loss: 20.4, cls_loss: 0.6, lr: 1.240e-03, size: 256, ETA: 1:57:03
2025-08-28 13:26:19.386 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 254/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 7.2, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.8, lr: 1.239e-03, size: 544, ETA: 1:57:00
2025-08-28 13:26:20.841 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:26:26.926 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:26:27.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:26:28.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5823
2025-08-28 13:26:28.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4698
2025-08-28 13:26:28.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3342
2025-08-28 13:26:28.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4621
2025-08-28 13:26:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:26:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:26:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-08-28 13:26:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.470
2025-08-28 13:26:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-08-28 13:26:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.462
2025-08-28 13:26:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:26:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:26:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:26:28.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:26:28.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:26:28.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:26:28.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:26:28.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:26:28.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:26:29.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:26:30.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:26:31.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:26:31.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:26:32.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:26:33.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:26:34.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:26:34.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:26:35.654 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:26:35.654 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:26:35.654 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:26:35.654 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:26:35.661 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.96 ms, Average inference time: 7.12 ms

2025-08-28 13:26:35.663 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:26:35.748 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:26:35.830 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch255
2025-08-28 13:26:38.668 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 255/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.238e-03, size: 576, ETA: 1:56:55
2025-08-28 13:26:41.767 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 255/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 0.7, lr: 1.237e-03, size: 576, ETA: 1:56:52
2025-08-28 13:26:44.771 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 255/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 1.236e-03, size: 384, ETA: 1:56:48
2025-08-28 13:26:47.826 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 255/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 1.235e-03, size: 480, ETA: 1:56:45
2025-08-28 13:26:50.889 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 255/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 1.235e-03, size: 352, ETA: 1:56:42
2025-08-28 13:26:53.912 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 255/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.5, lr: 1.234e-03, size: 448, ETA: 1:56:38
2025-08-28 13:26:55.295 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:27:01.442 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:27:02.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:27:02.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5584
2025-08-28 13:27:02.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4918
2025-08-28 13:27:02.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3795
2025-08-28 13:27:02.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4766
2025-08-28 13:27:02.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:27:02.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:27:02.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.558
2025-08-28 13:27:02.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.492
2025-08-28 13:27:02.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-08-28 13:27:02.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.477
2025-08-28 13:27:02.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:27:02.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:27:02.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:27:02.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:27:02.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:27:02.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:27:02.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:27:02.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:27:02.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:27:03.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:27:04.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:27:05.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:27:05.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:27:06.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:27:07.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:27:07.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:27:08.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:27:09.397 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:27:09.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:27:09.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:27:09.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:27:09.405 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.93 ms, Average inference time: 7.09 ms

2025-08-28 13:27:09.406 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:27:09.489 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:27:09.570 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch256
2025-08-28 13:27:12.424 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 256/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 1.233e-03, size: 416, ETA: 1:56:33
2025-08-28 13:27:15.572 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 256/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 1.232e-03, size: 512, ETA: 1:56:30
2025-08-28 13:27:18.647 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 256/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.231e-03, size: 352, ETA: 1:56:27
2025-08-28 13:27:21.921 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 256/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 1.230e-03, size: 576, ETA: 1:56:24
2025-08-28 13:27:25.062 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 256/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.5, cls_loss: 0.9, lr: 1.229e-03, size: 512, ETA: 1:56:20
2025-08-28 13:27:28.117 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 256/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 1.229e-03, size: 480, ETA: 1:56:17
2025-08-28 13:27:29.554 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:27:35.732 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:27:36.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:27:37.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5829
2025-08-28 13:27:37.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5061
2025-08-28 13:27:37.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3835
2025-08-28 13:27:37.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4908
2025-08-28 13:27:37.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:27:37.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:27:37.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.583
2025-08-28 13:27:37.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-08-28 13:27:37.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-28 13:27:37.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.491
2025-08-28 13:27:37.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:27:37.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:27:37.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:27:37.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:27:37.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:27:37.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:27:37.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:27:37.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:27:37.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:27:38.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:27:39.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:27:39.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:27:40.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:27:41.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:27:42.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:27:43.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:27:43.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:27:44.740 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:27:44.740 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:27:44.741 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:27:44.741 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:27:44.748 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.92 ms, Average inference time: 7.17 ms

2025-08-28 13:27:44.749 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:27:44.834 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:27:44.916 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch257
2025-08-28 13:27:47.755 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 257/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.228e-03, size: 480, ETA: 1:56:12
2025-08-28 13:27:50.848 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 257/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.7, lr: 1.227e-03, size: 512, ETA: 1:56:09
2025-08-28 13:27:53.976 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 257/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 1.226e-03, size: 256, ETA: 1:56:06
2025-08-28 13:27:57.043 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 257/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.8, lr: 1.225e-03, size: 256, ETA: 1:56:02
2025-08-28 13:28:00.075 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 257/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.9, lr: 1.224e-03, size: 544, ETA: 1:55:59
2025-08-28 13:28:03.107 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 257/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 1.0, lr: 1.224e-03, size: 512, ETA: 1:55:56
2025-08-28 13:28:04.469 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:28:10.681 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:28:11.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:28:11.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5464
2025-08-28 13:28:11.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4759
2025-08-28 13:28:11.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3819
2025-08-28 13:28:11.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4681
2025-08-28 13:28:11.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:28:11.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:28:11.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.546
2025-08-28 13:28:11.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-08-28 13:28:11.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-08-28 13:28:11.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.468
2025-08-28 13:28:11.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:28:11.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:28:11.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:28:11.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:28:11.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:28:11.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:28:11.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:28:11.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:28:11.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:28:12.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:28:12.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:28:12.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:28:13.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:28:13.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:28:13.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:28:14.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:28:14.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:28:15.088 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:28:15.088 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:28:15.089 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:28:15.089 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:28:15.096 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.88 ms, Average inference time: 7.20 ms

2025-08-28 13:28:15.097 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:28:15.184 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:28:15.263 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch258
2025-08-28 13:28:18.149 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 258/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 1.1, lr: 1.222e-03, size: 384, ETA: 1:55:51
2025-08-28 13:28:21.174 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 258/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.8, lr: 1.222e-03, size: 256, ETA: 1:55:47
2025-08-28 13:28:24.228 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 258/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.221e-03, size: 576, ETA: 1:55:44
2025-08-28 13:28:27.326 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 258/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 9.1, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 4.3, cls_loss: 0.9, lr: 1.220e-03, size: 288, ETA: 1:55:41
2025-08-28 13:28:30.403 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 258/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 1.219e-03, size: 416, ETA: 1:55:37
2025-08-28 13:28:33.492 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 258/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.6, lr: 1.218e-03, size: 448, ETA: 1:55:34
2025-08-28 13:28:34.822 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:28:40.993 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:28:42.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:28:42.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5879
2025-08-28 13:28:42.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5223
2025-08-28 13:28:42.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3763
2025-08-28 13:28:42.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4955
2025-08-28 13:28:42.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:28:42.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:28:42.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-08-28 13:28:42.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-08-28 13:28:42.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-08-28 13:28:42.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-08-28 13:28:42.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:28:42.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:28:42.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:28:42.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:28:42.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:28:42.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:28:42.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:28:42.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:28:42.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:28:43.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:28:44.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:28:45.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:28:46.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:28:47.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:28:48.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:28:49.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:28:50.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:28:51.269 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:28:51.269 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:28:51.269 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 13:28:51.269 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:28:51.277 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.94 ms, Average inference time: 7.05 ms

2025-08-28 13:28:51.278 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:28:51.404 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:28:51.477 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch259
2025-08-28 13:28:54.320 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 259/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.7, lr: 1.217e-03, size: 480, ETA: 1:55:29
2025-08-28 13:28:57.325 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 259/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.217e-03, size: 512, ETA: 1:55:25
2025-08-28 13:29:00.314 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 259/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 1.216e-03, size: 448, ETA: 1:55:22
2025-08-28 13:29:03.307 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 259/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.9, lr: 1.215e-03, size: 384, ETA: 1:55:19
2025-08-28 13:29:06.259 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 259/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.003s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.8, lr: 1.214e-03, size: 288, ETA: 1:55:15
2025-08-28 13:29:09.276 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 259/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 1.213e-03, size: 256, ETA: 1:55:12
2025-08-28 13:29:10.568 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:29:16.793 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:29:17.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:29:18.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5778
2025-08-28 13:29:18.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4902
2025-08-28 13:29:18.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3505
2025-08-28 13:29:18.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4728
2025-08-28 13:29:18.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:29:18.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:29:18.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-08-28 13:29:18.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-28 13:29:18.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.351
2025-08-28 13:29:18.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.473
2025-08-28 13:29:18.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:29:18.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:29:18.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:29:18.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:29:18.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:29:18.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:29:18.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:29:18.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:29:18.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:29:19.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:29:20.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:29:21.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:29:22.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:29:23.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:29:23.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:29:24.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:29:25.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:29:26.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:29:26.398 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:29:26.399 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:29:26.399 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:29:26.412 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.93 ms, Average inference time: 7.12 ms

2025-08-28 13:29:26.413 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:29:26.561 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:29:26.658 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch260
2025-08-28 13:29:29.661 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 260/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 1.212e-03, size: 512, ETA: 1:55:07
2025-08-28 13:29:32.824 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 260/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 3.1, cls_loss: 1.0, lr: 1.211e-03, size: 576, ETA: 1:55:04
2025-08-28 13:29:35.969 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 260/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 1.211e-03, size: 480, ETA: 1:55:00
2025-08-28 13:29:39.037 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 260/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 9.1, iou_loss: 3.3, l1_loss: 1.3, conf_loss: 3.7, cls_loss: 0.8, lr: 1.210e-03, size: 384, ETA: 1:54:57
2025-08-28 13:29:42.039 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 260/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.006s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.5, lr: 1.209e-03, size: 448, ETA: 1:54:54
2025-08-28 13:29:45.147 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 260/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.8, lr: 1.208e-03, size: 512, ETA: 1:54:51
2025-08-28 13:29:46.507 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:29:52.725 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:29:53.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:29:54.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5711
2025-08-28 13:29:54.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4854
2025-08-28 13:29:54.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3606
2025-08-28 13:29:54.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4724
2025-08-28 13:29:54.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:29:54.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:29:54.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.571
2025-08-28 13:29:54.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-08-28 13:29:54.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-08-28 13:29:54.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.472
2025-08-28 13:29:54.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:29:54.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:29:54.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:29:54.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:29:54.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:29:54.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:29:54.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:29:54.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:29:54.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:29:54.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:29:55.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:29:56.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:29:56.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:29:57.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:29:58.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:29:58.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:29:59.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:30:00.070 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:30:00.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:30:00.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:30:00.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:30:00.078 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.92 ms, Average inference time: 7.18 ms

2025-08-28 13:30:00.079 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:30:00.162 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:30:00.242 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch261
2025-08-28 13:30:03.086 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 261/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 1.8, cls_loss: 0.6, lr: 1.207e-03, size: 512, ETA: 1:54:45
2025-08-28 13:30:06.118 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 261/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 1.0, lr: 1.206e-03, size: 320, ETA: 1:54:42
2025-08-28 13:30:09.354 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 261/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.1, cls_loss: 0.6, lr: 1.206e-03, size: 576, ETA: 1:54:39
2025-08-28 13:30:12.553 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 261/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.7, lr: 1.205e-03, size: 352, ETA: 1:54:36
2025-08-28 13:30:15.672 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 261/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.6, lr: 1.204e-03, size: 512, ETA: 1:54:33
2025-08-28 13:30:18.633 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 261/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.3, cls_loss: 0.7, lr: 1.203e-03, size: 512, ETA: 1:54:29
2025-08-28 13:30:19.950 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:30:26.172 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:30:27.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:30:27.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5756
2025-08-28 13:30:28.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4889
2025-08-28 13:30:28.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3385
2025-08-28 13:30:28.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4677
2025-08-28 13:30:28.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:30:28.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:30:28.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-08-28 13:30:28.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-08-28 13:30:28.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.339
2025-08-28 13:30:28.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.468
2025-08-28 13:30:28.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:30:28.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:30:28.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:30:28.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:30:28.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:30:28.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:30:28.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:30:28.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:30:28.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:30:29.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:30:29.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:30:30.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:30:31.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:30:32.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:30:33.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:30:34.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:30:35.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:30:36.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:30:36.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:30:36.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:30:36.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:30:36.054 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.93 ms, Average inference time: 7.03 ms

2025-08-28 13:30:36.055 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:30:36.189 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:30:36.309 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch262
2025-08-28 13:30:39.319 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 262/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 3.7, cls_loss: 0.9, lr: 1.202e-03, size: 384, ETA: 1:54:24
2025-08-28 13:30:42.282 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 262/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.8, lr: 1.201e-03, size: 544, ETA: 1:54:21
2025-08-28 13:30:45.356 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 262/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.6, lr: 1.200e-03, size: 576, ETA: 1:54:18
2025-08-28 13:30:48.498 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 262/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 1.0, lr: 1.200e-03, size: 576, ETA: 1:54:14
2025-08-28 13:30:51.591 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 262/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.199e-03, size: 448, ETA: 1:54:11
2025-08-28 13:30:54.561 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 262/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 2.3, cls_loss: 0.7, lr: 1.198e-03, size: 320, ETA: 1:54:08
2025-08-28 13:30:55.883 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:31:01.909 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:31:02.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:31:02.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5719
2025-08-28 13:31:02.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5145
2025-08-28 13:31:02.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3676
2025-08-28 13:31:02.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4847
2025-08-28 13:31:02.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:31:02.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:31:02.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-08-28 13:31:02.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 13:31:02.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-28 13:31:02.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.485
2025-08-28 13:31:02.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:31:02.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:31:02.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:31:02.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:31:02.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:31:02.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:31:02.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:31:02.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:31:02.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:31:03.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:31:03.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:31:04.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:31:04.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:31:05.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:31:05.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:31:05.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:31:06.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:31:06.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:31:06.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:31:06.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:31:06.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:31:06.778 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.89 ms, Average inference time: 7.17 ms

2025-08-28 13:31:06.779 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:31:06.862 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:31:06.940 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch263
2025-08-28 13:31:09.861 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 263/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 1.197e-03, size: 448, ETA: 1:54:03
2025-08-28 13:31:12.852 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 263/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 1.196e-03, size: 256, ETA: 1:53:59
2025-08-28 13:31:15.952 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 263/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.8, lr: 1.195e-03, size: 320, ETA: 1:53:56
2025-08-28 13:31:19.038 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 263/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.7, lr: 1.194e-03, size: 480, ETA: 1:53:53
2025-08-28 13:31:22.220 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 263/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 1.0, lr: 1.194e-03, size: 544, ETA: 1:53:50
2025-08-28 13:31:25.355 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 263/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 3.5, cls_loss: 0.8, lr: 1.193e-03, size: 544, ETA: 1:53:46
2025-08-28 13:31:26.723 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:31:32.797 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:31:33.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:31:33.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5027
2025-08-28 13:31:33.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4335
2025-08-28 13:31:33.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3399
2025-08-28 13:31:33.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4254
2025-08-28 13:31:33.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:31:33.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:31:33.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-08-28 13:31:33.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.433
2025-08-28 13:31:33.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-08-28 13:31:33.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.425
2025-08-28 13:31:33.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:31:33.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:31:33.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:31:33.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:31:33.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:31:33.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:31:33.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:31:33.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:31:33.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:31:34.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:31:34.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:31:35.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:31:35.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:31:36.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:31:36.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:31:37.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:31:37.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:31:37.915 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:31:37.915 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 13:31:37.916 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 13:31:37.916 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:31:37.928 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.91 ms, Average inference time: 7.09 ms

2025-08-28 13:31:37.929 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:31:38.077 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:31:38.186 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch264
2025-08-28 13:31:41.281 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 264/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.8, lr: 1.192e-03, size: 384, ETA: 1:53:42
2025-08-28 13:31:44.461 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 264/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.191e-03, size: 512, ETA: 1:53:39
2025-08-28 13:31:47.395 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 264/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 1.190e-03, size: 288, ETA: 1:53:35
2025-08-28 13:31:50.411 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 264/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.189e-03, size: 384, ETA: 1:53:32
2025-08-28 13:31:53.452 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 264/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.8, lr: 1.189e-03, size: 416, ETA: 1:53:28
2025-08-28 13:31:56.525 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 264/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 1.188e-03, size: 384, ETA: 1:53:25
2025-08-28 13:31:57.904 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:32:03.970 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:32:04.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:32:05.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5714
2025-08-28 13:32:05.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4978
2025-08-28 13:32:05.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3831
2025-08-28 13:32:05.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4841
2025-08-28 13:32:05.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:32:05.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:32:05.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.571
2025-08-28 13:32:05.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-28 13:32:05.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-28 13:32:05.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-08-28 13:32:05.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:32:05.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:32:05.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:32:05.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:32:05.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:32:05.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:32:05.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:32:05.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:32:05.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:32:05.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:32:06.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:32:07.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:32:07.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:32:08.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:32:08.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:32:09.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:32:10.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:32:10.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:32:10.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 13:32:10.670 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:32:10.670 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:32:10.677 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.34 ms, Average NMS time: 0.93 ms, Average inference time: 7.27 ms

2025-08-28 13:32:10.678 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:32:10.760 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:32:10.840 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch265
2025-08-28 13:32:13.647 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 265/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.138s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 1.187e-03, size: 448, ETA: 1:53:20
2025-08-28 13:32:16.719 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 265/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 1.186e-03, size: 384, ETA: 1:53:17
2025-08-28 13:32:19.728 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 265/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 9.4, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 4.3, cls_loss: 0.8, lr: 1.185e-03, size: 512, ETA: 1:53:13
2025-08-28 13:32:22.780 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 265/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 1.184e-03, size: 384, ETA: 1:53:10
2025-08-28 13:32:25.817 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 265/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.8, lr: 1.183e-03, size: 384, ETA: 1:53:07
2025-08-28 13:32:28.941 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 265/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.7, lr: 1.183e-03, size: 416, ETA: 1:53:04
2025-08-28 13:32:30.285 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:32:36.482 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:32:37.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:32:38.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5762
2025-08-28 13:32:38.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5145
2025-08-28 13:32:38.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3687
2025-08-28 13:32:38.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4864
2025-08-28 13:32:38.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:32:38.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:32:38.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-08-28 13:32:38.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.514
2025-08-28 13:32:38.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-28 13:32:38.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.486
2025-08-28 13:32:38.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:32:38.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:32:38.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:32:38.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:32:38.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:32:38.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:32:38.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:32:38.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:32:38.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:32:39.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:32:39.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:32:40.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:32:41.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:32:42.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:32:43.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:32:43.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:32:44.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:32:45.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:32:45.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:32:45.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:32:45.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:32:45.597 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.98 ms, Average inference time: 7.10 ms

2025-08-28 13:32:45.599 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:32:45.680 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:32:45.815 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch266
2025-08-28 13:32:48.589 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 266/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.136s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 1.0, lr: 1.181e-03, size: 288, ETA: 1:52:58
2025-08-28 13:32:51.693 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 266/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.8, lr: 1.181e-03, size: 576, ETA: 1:52:55
2025-08-28 13:32:54.858 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 266/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.180e-03, size: 288, ETA: 1:52:52
2025-08-28 13:32:57.801 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 266/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.8, lr: 1.179e-03, size: 320, ETA: 1:52:48
2025-08-28 13:33:00.821 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 266/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 1.178e-03, size: 480, ETA: 1:52:45
2025-08-28 13:33:03.854 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 266/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.177e-03, size: 544, ETA: 1:52:42
2025-08-28 13:33:05.293 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:33:11.418 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:33:12.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:33:12.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5814
2025-08-28 13:33:12.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5118
2025-08-28 13:33:12.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3638
2025-08-28 13:33:12.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4856
2025-08-28 13:33:12.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:33:12.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:33:12.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.581
2025-08-28 13:33:12.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-08-28 13:33:12.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-08-28 13:33:12.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.486
2025-08-28 13:33:12.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:33:12.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:33:12.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:33:12.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:33:12.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:33:12.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:33:12.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:33:12.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:33:12.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:33:13.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:33:14.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:33:14.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:33:15.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:33:16.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:33:16.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:33:17.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:33:18.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:33:19.065 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:33:19.065 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:33:19.066 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:33:19.066 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:33:19.073 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.92 ms, Average inference time: 7.05 ms

2025-08-28 13:33:19.074 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:33:19.150 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:33:19.230 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch267
2025-08-28 13:33:22.070 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 267/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 1.176e-03, size: 512, ETA: 1:52:37
2025-08-28 13:33:25.136 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 267/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 1.175e-03, size: 416, ETA: 1:52:34
2025-08-28 13:33:28.113 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 267/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.6, lr: 1.175e-03, size: 544, ETA: 1:52:30
2025-08-28 13:33:31.154 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 267/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 1.174e-03, size: 384, ETA: 1:52:27
2025-08-28 13:33:34.161 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 267/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.7, lr: 1.173e-03, size: 416, ETA: 1:52:23
2025-08-28 13:33:37.099 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 267/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 0.7, lr: 1.172e-03, size: 480, ETA: 1:52:20
2025-08-28 13:33:38.488 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:33:44.694 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:33:45.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:33:45.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5512
2025-08-28 13:33:46.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5017
2025-08-28 13:33:46.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3416
2025-08-28 13:33:46.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4648
2025-08-28 13:33:46.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:33:46.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:33:46.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-08-28 13:33:46.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-08-28 13:33:46.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.342
2025-08-28 13:33:46.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.465
2025-08-28 13:33:46.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:33:46.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:33:46.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:33:46.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:33:46.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:33:46.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:33:46.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:33:46.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:33:46.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:33:46.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:33:47.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:33:47.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:33:48.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:33:49.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:33:49.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:33:50.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:33:51.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:33:51.693 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:33:51.693 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:33:51.693 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:33:51.693 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:33:51.701 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.93 ms, Average inference time: 7.21 ms

2025-08-28 13:33:51.702 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:33:51.790 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:33:51.871 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch268
2025-08-28 13:33:54.882 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 268/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.9, lr: 1.171e-03, size: 320, ETA: 1:52:15
2025-08-28 13:33:57.888 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 268/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.8, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.6, lr: 1.170e-03, size: 576, ETA: 1:52:12
2025-08-28 13:34:01.007 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 268/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.9, cls_loss: 0.8, lr: 1.170e-03, size: 576, ETA: 1:52:09
2025-08-28 13:34:04.171 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 268/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.169e-03, size: 320, ETA: 1:52:05
2025-08-28 13:34:07.049 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 268/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 1.168e-03, size: 256, ETA: 1:52:02
2025-08-28 13:34:09.970 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 268/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 1.167e-03, size: 448, ETA: 1:51:58
2025-08-28 13:34:11.310 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:34:17.521 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:34:18.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:34:18.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5811
2025-08-28 13:34:18.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4840
2025-08-28 13:34:18.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3937
2025-08-28 13:34:18.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4863
2025-08-28 13:34:18.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:34:18.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:34:18.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.581
2025-08-28 13:34:18.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-28 13:34:18.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-08-28 13:34:18.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.486
2025-08-28 13:34:18.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:34:18.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:34:18.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:34:18.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:34:18.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:34:18.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:34:18.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:34:18.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:34:18.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:34:19.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:34:20.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:34:20.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:34:21.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:34:22.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:34:22.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:34:23.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:34:23.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:34:24.550 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:34:24.550 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:34:24.550 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:34:24.550 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:34:24.557 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.95 ms, Average inference time: 7.20 ms

2025-08-28 13:34:24.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:34:24.642 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:34:24.766 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch269
2025-08-28 13:34:27.540 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 269/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.137s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 1.166e-03, size: 480, ETA: 1:51:53
2025-08-28 13:34:30.588 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 269/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.5, cls_loss: 0.7, lr: 1.165e-03, size: 320, ETA: 1:51:50
2025-08-28 13:34:33.752 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 269/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.7, lr: 1.164e-03, size: 256, ETA: 1:51:47
2025-08-28 13:34:36.619 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 269/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 1.2, lr: 1.164e-03, size: 288, ETA: 1:51:43
2025-08-28 13:34:39.772 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 269/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.8, lr: 1.163e-03, size: 256, ETA: 1:51:40
2025-08-28 13:34:42.686 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 269/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 1.162e-03, size: 256, ETA: 1:51:37
2025-08-28 13:34:44.036 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:34:50.199 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:34:51.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:34:52.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5589
2025-08-28 13:34:52.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4929
2025-08-28 13:34:52.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3805
2025-08-28 13:34:52.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4775
2025-08-28 13:34:52.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:34:52.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:34:52.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-08-28 13:34:52.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-08-28 13:34:52.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-08-28 13:34:52.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.477
2025-08-28 13:34:52.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:34:52.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:34:52.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:34:52.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:34:52.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:34:52.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:34:52.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:34:52.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:34:52.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:34:53.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:34:54.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:34:55.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:34:56.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:34:57.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:34:58.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:35:00.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:35:01.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:35:02.179 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:35:02.179 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:35:02.179 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:35:02.179 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:35:02.186 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.97 ms, Average inference time: 7.26 ms

2025-08-28 13:35:02.188 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:35:02.269 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:35:02.355 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch270
2025-08-28 13:35:05.230 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 270/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.161e-03, size: 256, ETA: 1:51:32
2025-08-28 13:35:08.181 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 270/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 1.160e-03, size: 320, ETA: 1:51:28
2025-08-28 13:35:11.293 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 270/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 3.4, cls_loss: 0.9, lr: 1.159e-03, size: 544, ETA: 1:51:25
2025-08-28 13:35:14.288 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 270/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.158e-03, size: 544, ETA: 1:51:22
2025-08-28 13:35:17.441 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 270/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.0, cls_loss: 0.6, lr: 1.158e-03, size: 352, ETA: 1:51:18
2025-08-28 13:35:20.478 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 270/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.6, lr: 1.157e-03, size: 576, ETA: 1:51:15
2025-08-28 13:35:21.926 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:35:28.096 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:35:28.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:35:29.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5794
2025-08-28 13:35:29.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5093
2025-08-28 13:35:29.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3883
2025-08-28 13:35:29.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4923
2025-08-28 13:35:29.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:35:29.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:35:29.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-08-28 13:35:29.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-08-28 13:35:29.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-08-28 13:35:29.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-08-28 13:35:29.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:35:29.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:35:29.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:35:29.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:35:29.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:35:29.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:35:29.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:35:29.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:35:29.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:35:29.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:35:30.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:35:30.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:35:31.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:35:32.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:35:32.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:35:33.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:35:33.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:35:34.221 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:35:34.221 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:35:34.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:35:34.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:35:34.228 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.88 ms, Average inference time: 7.11 ms

2025-08-28 13:35:34.229 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:35:34.310 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:35:34.390 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch271
2025-08-28 13:35:37.237 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 271/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.5, lr: 1.156e-03, size: 352, ETA: 1:51:10
2025-08-28 13:35:40.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 271/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.6, lr: 1.155e-03, size: 448, ETA: 1:51:07
2025-08-28 13:35:43.291 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 271/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 1.154e-03, size: 448, ETA: 1:51:04
2025-08-28 13:35:46.451 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 271/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.9, lr: 1.153e-03, size: 256, ETA: 1:51:00
2025-08-28 13:35:49.452 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 271/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.7, lr: 1.152e-03, size: 384, ETA: 1:50:57
2025-08-28 13:35:52.535 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 271/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 1.152e-03, size: 256, ETA: 1:50:54
2025-08-28 13:35:53.864 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:35:59.953 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:36:00.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:36:01.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5609
2025-08-28 13:36:01.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4983
2025-08-28 13:36:01.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3569
2025-08-28 13:36:01.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4720
2025-08-28 13:36:01.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:36:01.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:36:01.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.561
2025-08-28 13:36:01.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-28 13:36:01.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.357
2025-08-28 13:36:01.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.472
2025-08-28 13:36:01.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:36:01.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:36:01.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:36:01.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:36:01.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:36:01.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:36:01.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:36:01.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:36:01.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:36:02.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:36:02.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:36:03.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:36:04.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:36:04.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:36:05.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:36:06.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:36:06.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:36:07.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:36:07.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:36:07.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:36:07.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:36:07.610 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.92 ms, Average inference time: 7.10 ms

2025-08-28 13:36:07.611 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:36:07.745 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:36:07.818 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch272
2025-08-28 13:36:10.846 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 272/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 1.150e-03, size: 352, ETA: 1:50:49
2025-08-28 13:36:13.878 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 272/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 3.0, cls_loss: 0.8, lr: 1.150e-03, size: 544, ETA: 1:50:46
2025-08-28 13:36:16.873 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 272/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 8.8, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.9, cls_loss: 1.0, lr: 1.149e-03, size: 416, ETA: 1:50:42
2025-08-28 13:36:19.800 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 272/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 9.2, iou_loss: 3.6, l1_loss: 1.3, conf_loss: 3.5, cls_loss: 0.9, lr: 1.148e-03, size: 384, ETA: 1:50:39
2025-08-28 13:36:22.766 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 272/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.9, lr: 1.147e-03, size: 288, ETA: 1:50:35
2025-08-28 13:36:25.787 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 272/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 1.146e-03, size: 256, ETA: 1:50:32
2025-08-28 13:36:27.184 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:36:33.515 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:36:34.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:36:34.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5621
2025-08-28 13:36:34.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5235
2025-08-28 13:36:34.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4002
2025-08-28 13:36:34.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4953
2025-08-28 13:36:34.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:36:34.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:36:34.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.562
2025-08-28 13:36:34.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-08-28 13:36:34.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-28 13:36:34.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-08-28 13:36:34.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:36:34.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:36:34.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:36:34.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:36:34.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:36:34.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:36:34.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:36:34.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:36:34.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:36:35.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:36:35.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:36:36.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:36:36.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:36:37.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:36:38.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:36:38.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:36:39.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:36:39.619 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:36:39.619 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:36:39.619 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 13:36:39.620 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:36:39.627 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.91 ms, Average inference time: 7.13 ms

2025-08-28 13:36:39.628 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:36:39.771 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:36:39.843 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch273
2025-08-28 13:36:42.737 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 273/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.145e-03, size: 288, ETA: 1:50:27
2025-08-28 13:36:45.746 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 273/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.6, lr: 1.144e-03, size: 256, ETA: 1:50:24
2025-08-28 13:36:48.747 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 273/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.144e-03, size: 448, ETA: 1:50:20
2025-08-28 13:36:51.745 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 273/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.143e-03, size: 256, ETA: 1:50:17
2025-08-28 13:36:54.872 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 273/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.142e-03, size: 512, ETA: 1:50:14
2025-08-28 13:36:57.950 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 273/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.141e-03, size: 288, ETA: 1:50:11
2025-08-28 13:36:59.261 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:37:05.455 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:37:06.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:37:07.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5556
2025-08-28 13:37:07.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4625
2025-08-28 13:37:07.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2575
2025-08-28 13:37:07.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4252
2025-08-28 13:37:07.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:37:07.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:37:07.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.556
2025-08-28 13:37:07.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.463
2025-08-28 13:37:07.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.257
2025-08-28 13:37:07.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.425
2025-08-28 13:37:07.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:37:07.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:37:07.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:37:07.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:37:07.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:37:07.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:37:07.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:37:07.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:37:07.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:37:08.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:37:09.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:37:10.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:37:11.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:37:12.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:37:13.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:37:14.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:37:15.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:37:16.964 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:37:16.964 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:37:16.964 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 13:37:16.965 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:37:16.978 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.92 ms, Average inference time: 7.22 ms

2025-08-28 13:37:16.979 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:37:17.121 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:37:17.219 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch274
2025-08-28 13:37:20.044 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 274/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 2.0, cls_loss: 0.5, lr: 1.140e-03, size: 320, ETA: 1:50:06
2025-08-28 13:37:23.179 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 274/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 3.2, cls_loss: 0.6, lr: 1.139e-03, size: 576, ETA: 1:50:02
2025-08-28 13:37:26.202 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 274/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.138e-03, size: 288, ETA: 1:49:59
2025-08-28 13:37:29.158 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 274/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.8, lr: 1.138e-03, size: 256, ETA: 1:49:56
2025-08-28 13:37:32.284 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 274/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.137e-03, size: 448, ETA: 1:49:52
2025-08-28 13:37:35.250 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 274/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.136e-03, size: 544, ETA: 1:49:49
2025-08-28 13:37:36.582 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:37:42.788 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:37:43.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:37:44.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5837
2025-08-28 13:37:44.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4969
2025-08-28 13:37:44.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3709
2025-08-28 13:37:44.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4838
2025-08-28 13:37:44.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:37:44.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:37:44.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-08-28 13:37:44.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-08-28 13:37:44.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-08-28 13:37:44.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-08-28 13:37:44.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:37:44.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:37:44.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:37:44.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:37:44.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:37:44.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:37:44.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:37:44.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:37:44.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:37:45.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:37:45.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:37:46.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:37:46.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:37:47.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:37:48.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:37:48.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:37:49.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:37:50.150 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:37:50.150 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:37:50.150 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:37:50.150 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:37:50.157 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.94 ms, Average inference time: 7.07 ms

2025-08-28 13:37:50.158 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:37:50.235 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:37:50.315 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch275
2025-08-28 13:37:53.178 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 275/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.9, l1_loss: 0.8, conf_loss: 4.0, cls_loss: 0.8, lr: 1.135e-03, size: 352, ETA: 1:49:44
2025-08-28 13:37:56.173 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 275/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 2.7, cls_loss: 0.7, lr: 1.134e-03, size: 256, ETA: 1:49:41
2025-08-28 13:37:59.286 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 275/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 1.133e-03, size: 480, ETA: 1:49:37
2025-08-28 13:38:02.386 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 275/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.5, conf_loss: 1.7, cls_loss: 0.7, lr: 1.132e-03, size: 416, ETA: 1:49:34
2025-08-28 13:38:05.529 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 275/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 1.4, conf_loss: 2.3, cls_loss: 0.6, lr: 1.132e-03, size: 576, ETA: 1:49:31
2025-08-28 13:38:08.721 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 275/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 1.2, lr: 1.131e-03, size: 384, ETA: 1:49:28
2025-08-28 13:38:10.060 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:38:16.323 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:38:17.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:38:17.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5794
2025-08-28 13:38:17.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4574
2025-08-28 13:38:17.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3678
2025-08-28 13:38:17.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4682
2025-08-28 13:38:17.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:38:17.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:38:17.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-08-28 13:38:17.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.457
2025-08-28 13:38:17.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-28 13:38:17.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.468
2025-08-28 13:38:17.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:38:17.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:38:17.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:38:17.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:38:17.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:38:17.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:38:17.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:38:17.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:38:17.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:38:18.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:38:19.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:38:19.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:38:20.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:38:20.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:38:21.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:38:22.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:38:22.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:38:23.433 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:38:23.433 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:38:23.433 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:38:23.434 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:38:23.442 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.34 ms, Average NMS time: 0.93 ms, Average inference time: 7.26 ms

2025-08-28 13:38:23.443 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:38:23.558 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:38:23.652 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch276
2025-08-28 13:38:26.503 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 276/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.130e-03, size: 544, ETA: 1:49:23
2025-08-28 13:38:29.553 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 276/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 1.129e-03, size: 448, ETA: 1:49:20
2025-08-28 13:38:32.605 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 276/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 1.128e-03, size: 288, ETA: 1:49:16
2025-08-28 13:38:35.654 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 276/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.3, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 1.127e-03, size: 288, ETA: 1:49:13
2025-08-28 13:38:38.661 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 276/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.006s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 1.127e-03, size: 288, ETA: 1:49:10
2025-08-28 13:38:41.701 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 276/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.126e-03, size: 512, ETA: 1:49:06
2025-08-28 13:38:43.109 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:38:49.229 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:38:50.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:38:50.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5970
2025-08-28 13:38:50.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5146
2025-08-28 13:38:51.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3921
2025-08-28 13:38:51.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5012
2025-08-28 13:38:51.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:38:51.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:38:51.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.597
2025-08-28 13:38:51.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 13:38:51.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-28 13:38:51.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-08-28 13:38:51.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:38:51.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:38:51.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:38:51.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:38:51.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:38:51.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:38:51.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:38:51.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:38:51.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:38:51.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:38:52.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:38:53.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:38:54.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:38:55.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:38:55.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:38:56.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:38:57.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:38:58.271 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:38:58.271 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 13:38:58.271 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 13:38:58.271 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:38:58.278 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.95 ms, Average inference time: 7.15 ms

2025-08-28 13:38:58.281 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:38:58.363 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:38:58.441 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch277
2025-08-28 13:39:01.466 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 277/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 1.125e-03, size: 448, ETA: 1:49:02
2025-08-28 13:39:04.478 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 277/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.9, cls_loss: 0.8, lr: 1.124e-03, size: 480, ETA: 1:48:58
2025-08-28 13:39:07.473 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 277/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.123e-03, size: 512, ETA: 1:48:55
2025-08-28 13:39:10.622 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 277/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.9, lr: 1.122e-03, size: 512, ETA: 1:48:52
2025-08-28 13:39:13.627 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 277/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 1.121e-03, size: 256, ETA: 1:48:49
2025-08-28 13:39:16.580 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 277/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 1.120e-03, size: 288, ETA: 1:48:45
2025-08-28 13:39:17.920 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:39:24.025 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:39:24.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:39:24.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5554
2025-08-28 13:39:24.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4762
2025-08-28 13:39:24.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3720
2025-08-28 13:39:24.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4679
2025-08-28 13:39:24.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:39:24.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:39:24.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.555
2025-08-28 13:39:24.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-08-28 13:39:24.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-28 13:39:24.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.468
2025-08-28 13:39:24.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:39:24.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:39:24.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:39:24.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:39:24.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:39:24.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:39:24.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:39:24.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:39:24.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:39:25.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:39:25.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:39:25.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:39:26.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:39:26.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:39:27.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:39:27.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:39:27.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:39:28.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:39:28.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:39:28.207 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:39:28.207 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:39:28.214 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.87 ms, Average inference time: 7.15 ms

2025-08-28 13:39:28.215 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:39:28.296 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:39:28.375 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch278
2025-08-28 13:39:31.249 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 278/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 1.119e-03, size: 448, ETA: 1:48:40
2025-08-28 13:39:34.387 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 278/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.119e-03, size: 384, ETA: 1:48:37
2025-08-28 13:39:37.401 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 278/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 1.2, lr: 1.118e-03, size: 544, ETA: 1:48:34
2025-08-28 13:39:40.427 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 278/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.117e-03, size: 448, ETA: 1:48:30
2025-08-28 13:39:43.500 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 278/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 1.116e-03, size: 512, ETA: 1:48:27
2025-08-28 13:39:46.470 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 278/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 9.8, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 5.1, cls_loss: 1.0, lr: 1.115e-03, size: 352, ETA: 1:48:24
2025-08-28 13:39:47.762 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:39:53.965 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:39:55.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:39:55.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5728
2025-08-28 13:39:55.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5012
2025-08-28 13:39:55.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3549
2025-08-28 13:39:55.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4763
2025-08-28 13:39:55.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:39:55.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:39:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.573
2025-08-28 13:39:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-08-28 13:39:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.355
2025-08-28 13:39:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.476
2025-08-28 13:39:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:39:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:39:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:39:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:39:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:39:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:39:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:39:55.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:39:55.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:39:56.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:39:57.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:39:58.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:39:59.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:40:00.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:40:01.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:40:02.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:40:03.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:40:04.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:40:04.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:40:04.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:40:04.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:40:04.034 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.97 ms, Average inference time: 7.26 ms

2025-08-28 13:40:04.035 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:40:04.113 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:40:04.192 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch279
2025-08-28 13:40:07.014 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 279/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.138s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.114e-03, size: 256, ETA: 1:48:19
2025-08-28 13:40:10.107 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 279/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 1.113e-03, size: 480, ETA: 1:48:15
2025-08-28 13:40:13.143 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 279/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.8, lr: 1.113e-03, size: 384, ETA: 1:48:12
2025-08-28 13:40:16.287 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 279/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 1.112e-03, size: 480, ETA: 1:48:09
2025-08-28 13:40:19.335 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 279/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.8, lr: 1.111e-03, size: 384, ETA: 1:48:06
2025-08-28 13:40:22.291 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 279/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 8.6, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 2.1, lr: 1.110e-03, size: 544, ETA: 1:48:02
2025-08-28 13:40:23.691 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:40:29.768 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:40:30.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:40:30.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5302
2025-08-28 13:40:30.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4317
2025-08-28 13:40:30.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2946
2025-08-28 13:40:30.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4188
2025-08-28 13:40:30.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:40:30.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:40:30.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 13:40:30.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.432
2025-08-28 13:40:30.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-08-28 13:40:30.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.419
2025-08-28 13:40:30.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:40:30.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:40:30.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:40:30.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:40:30.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:40:30.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:40:30.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:40:30.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:40:30.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:40:31.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:40:31.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:40:32.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:40:32.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:40:33.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:40:33.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:40:34.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:40:34.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:40:35.277 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:40:35.277 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 13:40:35.278 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 13:40:35.278 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:40:35.284 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.87 ms, Average inference time: 7.19 ms

2025-08-28 13:40:35.285 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:40:35.389 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:40:35.466 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch280
2025-08-28 13:40:38.342 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 280/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 1.4, lr: 1.109e-03, size: 352, ETA: 1:47:57
2025-08-28 13:40:41.288 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 280/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 1.108e-03, size: 384, ETA: 1:47:54
2025-08-28 13:40:44.260 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 280/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 1.107e-03, size: 416, ETA: 1:47:51
2025-08-28 13:40:47.273 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 280/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 1.107e-03, size: 288, ETA: 1:47:47
2025-08-28 13:40:50.379 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 280/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.106e-03, size: 416, ETA: 1:47:44
2025-08-28 13:40:53.474 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 280/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 1.0, lr: 1.105e-03, size: 416, ETA: 1:47:41
2025-08-28 13:40:54.796 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:41:00.784 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:41:01.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:41:01.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5504
2025-08-28 13:41:01.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4749
2025-08-28 13:41:01.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3415
2025-08-28 13:41:01.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4556
2025-08-28 13:41:01.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:41:01.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:41:01.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.550
2025-08-28 13:41:01.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-08-28 13:41:01.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.342
2025-08-28 13:41:01.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.456
2025-08-28 13:41:01.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:41:01.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:41:01.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:41:01.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:41:01.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:41:01.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:41:01.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:41:01.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:41:01.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:41:02.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:41:02.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:41:02.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:41:03.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:41:03.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:41:03.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:41:04.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:41:04.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:41:05.019 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:41:05.020 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:41:05.020 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:41:05.020 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:41:05.029 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.86 ms, Average inference time: 6.95 ms

2025-08-28 13:41:05.030 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:41:05.131 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:41:05.213 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch281
2025-08-28 13:41:08.039 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 281/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 9.4, iou_loss: 3.3, l1_loss: 1.4, conf_loss: 4.0, cls_loss: 0.8, lr: 1.104e-03, size: 320, ETA: 1:47:36
2025-08-28 13:41:11.128 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 281/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.103e-03, size: 256, ETA: 1:47:33
2025-08-28 13:41:14.263 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 281/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 1.7, cls_loss: 0.7, lr: 1.102e-03, size: 448, ETA: 1:47:29
2025-08-28 13:41:17.209 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 281/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.101e-03, size: 512, ETA: 1:47:26
2025-08-28 13:41:20.265 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 281/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.100e-03, size: 416, ETA: 1:47:23
2025-08-28 13:41:23.280 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 281/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 1.100e-03, size: 416, ETA: 1:47:19
2025-08-28 13:41:24.666 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:41:30.883 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:41:31.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:41:32.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5417
2025-08-28 13:41:32.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4762
2025-08-28 13:41:32.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3563
2025-08-28 13:41:32.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4581
2025-08-28 13:41:32.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:41:32.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:41:32.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-08-28 13:41:32.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-08-28 13:41:32.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-08-28 13:41:32.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.458
2025-08-28 13:41:32.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:41:32.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:41:32.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:41:32.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:41:32.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:41:32.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:41:32.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:41:32.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:41:32.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:41:33.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:41:34.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:41:34.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:41:35.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:41:36.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:41:36.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:41:37.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:41:38.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:41:39.100 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:41:39.100 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 13:41:39.100 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:41:39.100 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:41:39.107 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.93 ms, Average inference time: 7.13 ms

2025-08-28 13:41:39.109 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:41:39.204 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:41:39.286 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch282
2025-08-28 13:41:42.156 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 282/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 1.099e-03, size: 384, ETA: 1:47:14
2025-08-28 13:41:45.168 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 282/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.5, lr: 1.098e-03, size: 288, ETA: 1:47:11
2025-08-28 13:41:48.327 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 282/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.097e-03, size: 384, ETA: 1:47:08
2025-08-28 13:41:51.369 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 282/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 1.096e-03, size: 320, ETA: 1:47:05
2025-08-28 13:41:54.508 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 282/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.7, lr: 1.095e-03, size: 576, ETA: 1:47:02
2025-08-28 13:41:57.550 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 282/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.7, lr: 1.094e-03, size: 320, ETA: 1:46:58
2025-08-28 13:41:58.830 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:42:04.993 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:42:06.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:42:07.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5861
2025-08-28 13:42:07.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4946
2025-08-28 13:42:07.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3525
2025-08-28 13:42:07.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4778
2025-08-28 13:42:07.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:42:07.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:42:07.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-08-28 13:42:07.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-08-28 13:42:07.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.353
2025-08-28 13:42:07.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-08-28 13:42:07.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:42:07.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:42:07.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:42:07.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:42:07.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:42:07.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:42:07.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:42:07.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:42:07.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:42:08.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:42:09.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:42:10.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:42:11.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:42:12.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:42:13.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:42:14.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:42:15.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:42:16.380 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:42:16.381 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:42:16.381 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:42:16.381 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:42:16.389 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.10 ms, Average NMS time: 0.90 ms, Average inference time: 6.99 ms

2025-08-28 13:42:16.390 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:42:16.511 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:42:16.585 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch283
2025-08-28 13:42:19.595 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 283/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.5, lr: 1.093e-03, size: 288, ETA: 1:46:53
2025-08-28 13:42:22.547 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 283/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.8, lr: 1.092e-03, size: 512, ETA: 1:46:50
2025-08-28 13:42:25.514 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 283/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 8.8, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 3.7, cls_loss: 0.8, lr: 1.092e-03, size: 352, ETA: 1:46:47
2025-08-28 13:42:28.499 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 283/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 1.091e-03, size: 384, ETA: 1:46:43
2025-08-28 13:42:31.673 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 283/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 1.8, cls_loss: 0.7, lr: 1.090e-03, size: 512, ETA: 1:46:40
2025-08-28 13:42:34.720 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 283/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.089e-03, size: 448, ETA: 1:46:37
2025-08-28 13:42:36.038 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:42:42.168 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:42:42.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:42:42.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5487
2025-08-28 13:42:42.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4506
2025-08-28 13:42:43.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3057
2025-08-28 13:42:43.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4350
2025-08-28 13:42:43.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:42:43.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:42:43.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.549
2025-08-28 13:42:43.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-08-28 13:42:43.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.306
2025-08-28 13:42:43.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.435
2025-08-28 13:42:43.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:42:43.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:42:43.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:42:43.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:42:43.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:42:43.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:42:43.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:42:43.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:42:43.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:42:43.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:42:43.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:42:44.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:42:44.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:42:44.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:42:45.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:42:45.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:42:45.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:42:46.213 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:42:46.214 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 13:42:46.214 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 13:42:46.214 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:42:46.221 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.81 ms, Average inference time: 7.06 ms

2025-08-28 13:42:46.222 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:42:46.368 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:42:46.438 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch284
2025-08-28 13:42:49.307 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 284/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.088e-03, size: 544, ETA: 1:46:32
2025-08-28 13:42:52.406 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 284/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 2.0, cls_loss: 0.6, lr: 1.087e-03, size: 576, ETA: 1:46:29
2025-08-28 13:42:55.476 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 284/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.7, lr: 1.086e-03, size: 352, ETA: 1:46:25
2025-08-28 13:42:58.552 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 284/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.086e-03, size: 320, ETA: 1:46:22
2025-08-28 13:43:01.762 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 284/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 0.7, lr: 1.085e-03, size: 448, ETA: 1:46:19
2025-08-28 13:43:04.740 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 284/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.084e-03, size: 384, ETA: 1:46:16
2025-08-28 13:43:06.083 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:43:12.387 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:43:13.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:43:14.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5804
2025-08-28 13:43:14.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4968
2025-08-28 13:43:14.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3690
2025-08-28 13:43:14.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4821
2025-08-28 13:43:14.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:43:14.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:43:14.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.580
2025-08-28 13:43:14.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-08-28 13:43:14.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-28 13:43:14.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.482
2025-08-28 13:43:14.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:43:14.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:43:14.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:43:14.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:43:14.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:43:14.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:43:14.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:43:14.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:43:14.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:43:15.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:43:15.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:43:16.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:43:17.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:43:18.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:43:19.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:43:20.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:43:20.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:43:21.754 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:43:21.755 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:43:21.755 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:43:21.755 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:43:21.762 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.94 ms, Average inference time: 7.17 ms

2025-08-28 13:43:21.766 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:43:21.844 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:43:21.927 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch285
2025-08-28 13:43:24.679 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 285/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.137s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.8, lr: 1.083e-03, size: 256, ETA: 1:46:11
2025-08-28 13:43:27.518 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 285/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 3.7, cls_loss: 0.7, lr: 1.082e-03, size: 448, ETA: 1:46:07
2025-08-28 13:43:30.603 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 285/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 1.0, lr: 1.081e-03, size: 576, ETA: 1:46:04
2025-08-28 13:43:33.747 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 285/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.7, lr: 1.080e-03, size: 288, ETA: 1:46:01
2025-08-28 13:43:36.722 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 285/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 1.080e-03, size: 256, ETA: 1:45:57
2025-08-28 13:43:39.674 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 285/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 9.2, iou_loss: 3.2, l1_loss: 1.6, conf_loss: 3.5, cls_loss: 0.9, lr: 1.079e-03, size: 352, ETA: 1:45:54
2025-08-28 13:43:41.063 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:43:47.238 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:43:47.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:43:48.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5435
2025-08-28 13:43:48.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4784
2025-08-28 13:43:48.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3679
2025-08-28 13:43:48.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4633
2025-08-28 13:43:48.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:43:48.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:43:48.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-08-28 13:43:48.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.478
2025-08-28 13:43:48.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-28 13:43:48.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.463
2025-08-28 13:43:48.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:43:48.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:43:48.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:43:48.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:43:48.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:43:48.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:43:48.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:43:48.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:43:48.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:43:49.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:43:49.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:43:50.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:43:50.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:43:51.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:43:51.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:43:52.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:43:52.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:43:53.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:43:53.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:43:53.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:43:53.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:43:53.581 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.91 ms, Average inference time: 7.14 ms

2025-08-28 13:43:53.582 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:43:53.662 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:43:53.744 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch286
2025-08-28 13:43:56.585 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 286/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.7, lr: 1.078e-03, size: 352, ETA: 1:45:49
2025-08-28 13:43:59.544 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 286/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.077e-03, size: 256, ETA: 1:45:46
2025-08-28 13:44:02.470 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 286/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 1.076e-03, size: 384, ETA: 1:45:42
2025-08-28 13:44:05.543 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 286/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 1.075e-03, size: 384, ETA: 1:45:39
2025-08-28 13:44:08.662 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 286/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.7, lr: 1.074e-03, size: 448, ETA: 1:45:36
2025-08-28 13:44:11.638 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 286/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 8.6, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.074e-03, size: 448, ETA: 1:45:33
2025-08-28 13:44:12.993 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:44:19.108 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:44:19.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:44:20.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5504
2025-08-28 13:44:20.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4486
2025-08-28 13:44:20.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2576
2025-08-28 13:44:20.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4189
2025-08-28 13:44:20.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:44:20.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:44:20.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.550
2025-08-28 13:44:20.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.449
2025-08-28 13:44:20.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.258
2025-08-28 13:44:20.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.419
2025-08-28 13:44:20.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:44:20.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:44:20.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:44:20.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:44:20.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:44:20.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:44:20.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:44:20.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:44:20.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:44:21.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:44:21.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:44:22.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:44:23.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:44:23.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:44:24.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:44:24.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:44:25.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:44:26.067 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:44:26.067 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-08-28 13:44:26.067 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 13:44:26.067 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:44:26.074 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.93 ms, Average inference time: 7.15 ms

2025-08-28 13:44:26.075 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:44:26.162 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:44:26.280 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch287
2025-08-28 13:44:29.189 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 287/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.6, lr: 1.072e-03, size: 256, ETA: 1:45:28
2025-08-28 13:44:32.152 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 287/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.072e-03, size: 352, ETA: 1:45:24
2025-08-28 13:44:35.146 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 287/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 1.071e-03, size: 544, ETA: 1:45:21
2025-08-28 13:44:38.166 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 287/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.6, lr: 1.070e-03, size: 544, ETA: 1:45:18
2025-08-28 13:44:41.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 287/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.8, lr: 1.069e-03, size: 576, ETA: 1:45:14
2025-08-28 13:44:44.349 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 287/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 1.068e-03, size: 448, ETA: 1:45:11
2025-08-28 13:44:45.753 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:44:51.878 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:44:52.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:44:53.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5702
2025-08-28 13:44:53.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5127
2025-08-28 13:44:53.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3629
2025-08-28 13:44:53.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4819
2025-08-28 13:44:53.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:44:53.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:44:53.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.570
2025-08-28 13:44:53.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-08-28 13:44:53.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-08-28 13:44:53.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.482
2025-08-28 13:44:53.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:44:53.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:44:53.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:44:53.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:44:53.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:44:53.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:44:53.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:44:53.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:44:53.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:44:54.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:44:55.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:44:55.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:44:56.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:44:57.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:44:58.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:44:58.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:44:59.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:45:00.214 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:45:00.215 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:45:00.215 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:45:00.215 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:45:00.222 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.94 ms, Average inference time: 7.13 ms

2025-08-28 13:45:00.223 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:45:00.309 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:45:00.390 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch288
2025-08-28 13:45:03.397 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 288/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 1.067e-03, size: 256, ETA: 1:45:06
2025-08-28 13:45:06.532 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 288/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.6, lr: 1.066e-03, size: 576, ETA: 1:45:03
2025-08-28 13:45:09.720 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 288/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 1.066e-03, size: 512, ETA: 1:45:00
2025-08-28 13:45:12.925 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 288/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.6, lr: 1.065e-03, size: 576, ETA: 1:44:57
2025-08-28 13:45:16.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 288/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 1.064e-03, size: 576, ETA: 1:44:54
2025-08-28 13:45:19.404 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 288/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 3.3, cls_loss: 0.6, lr: 1.063e-03, size: 544, ETA: 1:44:51
2025-08-28 13:45:20.807 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:45:26.930 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:45:27.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:45:28.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5789
2025-08-28 13:45:28.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5131
2025-08-28 13:45:28.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3722
2025-08-28 13:45:28.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4881
2025-08-28 13:45:28.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:45:28.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:45:28.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-08-28 13:45:28.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-08-28 13:45:28.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-28 13:45:28.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-08-28 13:45:28.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:45:28.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:45:28.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:45:28.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:45:28.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:45:28.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:45:28.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:45:28.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:45:28.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:45:29.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:45:29.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:45:30.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:45:31.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:45:31.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:45:32.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:45:32.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:45:33.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:45:34.254 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:45:34.254 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:45:34.254 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:45:34.254 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:45:34.261 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.92 ms, Average inference time: 7.19 ms

2025-08-28 13:45:34.263 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:45:34.341 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:45:34.420 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch289
2025-08-28 13:45:37.180 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 289/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.136s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.6, lr: 1.062e-03, size: 320, ETA: 1:44:46
2025-08-28 13:45:40.364 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 289/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 3.6, cls_loss: 0.9, lr: 1.061e-03, size: 448, ETA: 1:44:43
2025-08-28 13:45:43.364 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 289/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.060e-03, size: 512, ETA: 1:44:40
2025-08-28 13:45:46.526 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 289/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.5, lr: 1.060e-03, size: 544, ETA: 1:44:36
2025-08-28 13:45:49.644 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 289/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 3.8, cls_loss: 0.8, lr: 1.059e-03, size: 320, ETA: 1:44:33
2025-08-28 13:45:52.606 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 289/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.7, lr: 1.058e-03, size: 448, ETA: 1:44:30
2025-08-28 13:45:53.952 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:46:00.148 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:46:00.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:46:01.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5473
2025-08-28 13:46:01.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4022
2025-08-28 13:46:01.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3611
2025-08-28 13:46:01.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4368
2025-08-28 13:46:01.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:46:01.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:46:01.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.547
2025-08-28 13:46:01.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-28 13:46:01.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-08-28 13:46:01.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.437
2025-08-28 13:46:01.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:46:01.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:46:01.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:46:01.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:46:01.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:46:01.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:46:01.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:46:01.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:46:01.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:46:02.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:46:03.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:46:03.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:46:04.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:46:05.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:46:05.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:46:06.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:46:07.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:46:07.847 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:46:07.847 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 13:46:07.847 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 13:46:07.847 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:46:07.855 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.94 ms, Average inference time: 7.06 ms

2025-08-28 13:46:07.856 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:46:07.937 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:46:08.021 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch290
2025-08-28 13:46:11.047 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 290/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 1.057e-03, size: 384, ETA: 1:44:25
2025-08-28 13:46:14.079 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 290/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.056e-03, size: 256, ETA: 1:44:22
2025-08-28 13:46:17.018 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 290/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.7, lr: 1.055e-03, size: 256, ETA: 1:44:19
2025-08-28 13:46:20.023 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 290/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.054e-03, size: 448, ETA: 1:44:15
2025-08-28 13:46:23.045 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 290/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 1.054e-03, size: 352, ETA: 1:44:12
2025-08-28 13:46:26.052 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 290/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.6, lr: 1.053e-03, size: 544, ETA: 1:44:09
2025-08-28 13:46:27.392 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:46:33.538 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:46:34.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:46:34.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5854
2025-08-28 13:46:34.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4595
2025-08-28 13:46:34.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3761
2025-08-28 13:46:34.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4737
2025-08-28 13:46:34.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:46:34.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:46:34.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.585
2025-08-28 13:46:34.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-08-28 13:46:34.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-08-28 13:46:34.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.474
2025-08-28 13:46:34.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:46:34.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:46:34.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:46:34.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:46:34.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:46:34.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:46:34.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:46:34.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:46:34.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:46:35.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:46:36.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:46:36.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:46:37.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:46:38.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:46:38.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:46:39.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:46:40.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:46:40.846 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:46:40.846 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:46:40.847 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:46:40.847 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:46:40.854 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.95 ms, Average inference time: 7.17 ms

2025-08-28 13:46:40.857 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:46:40.986 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:46:41.060 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch291
2025-08-28 13:46:44.017 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 291/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.052e-03, size: 416, ETA: 1:44:04
2025-08-28 13:46:47.072 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 291/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 1.0, lr: 1.051e-03, size: 480, ETA: 1:44:01
2025-08-28 13:46:50.096 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 291/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 1.050e-03, size: 288, ETA: 1:43:57
2025-08-28 13:46:53.248 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 291/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.6, lr: 1.049e-03, size: 480, ETA: 1:43:54
2025-08-28 13:46:56.416 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 291/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 4.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 0.9, cls_loss: 0.6, lr: 1.048e-03, size: 352, ETA: 1:43:51
2025-08-28 13:46:59.546 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 291/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.047e-03, size: 384, ETA: 1:43:48
2025-08-28 13:47:00.868 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:47:06.995 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:47:08.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:47:09.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5754
2025-08-28 13:47:09.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4857
2025-08-28 13:47:09.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3500
2025-08-28 13:47:09.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4704
2025-08-28 13:47:09.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:47:09.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:47:09.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.575
2025-08-28 13:47:09.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-08-28 13:47:09.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.350
2025-08-28 13:47:09.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.470
2025-08-28 13:47:09.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:47:09.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:47:09.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:47:09.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:47:09.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:47:09.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:47:09.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:47:09.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:47:09.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:47:10.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:47:11.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:47:12.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:47:14.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:47:15.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:47:16.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:47:17.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:47:18.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:47:19.634 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:47:19.634 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:47:19.634 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:47:19.634 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:47:19.641 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.97 ms, Average inference time: 7.26 ms

2025-08-28 13:47:19.643 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:47:19.763 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:47:19.853 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch292
2025-08-28 13:47:22.767 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 292/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 1.046e-03, size: 544, ETA: 1:43:43
2025-08-28 13:47:25.714 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 292/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.045e-03, size: 448, ETA: 1:43:40
2025-08-28 13:47:28.664 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 292/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 1.045e-03, size: 352, ETA: 1:43:36
2025-08-28 13:47:31.652 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 292/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 1.4, cls_loss: 0.6, lr: 1.044e-03, size: 480, ETA: 1:43:33
2025-08-28 13:47:34.854 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 292/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 1.043e-03, size: 256, ETA: 1:43:30
2025-08-28 13:47:37.795 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 292/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.6, lr: 1.042e-03, size: 384, ETA: 1:43:26
2025-08-28 13:47:39.104 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:47:45.287 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:47:45.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:47:46.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5675
2025-08-28 13:47:46.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4751
2025-08-28 13:47:46.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4098
2025-08-28 13:47:46.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4841
2025-08-28 13:47:46.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:47:46.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:47:46.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.567
2025-08-28 13:47:46.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-08-28 13:47:46.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-08-28 13:47:46.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-08-28 13:47:46.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:47:46.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:47:46.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:47:46.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:47:46.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:47:46.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:47:46.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:47:46.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:47:46.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:47:46.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:47:47.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:47:47.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:47:48.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:47:48.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:47:49.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:47:49.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:47:49.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:47:50.362 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:47:50.362 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:47:50.362 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:47:50.362 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:47:50.369 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.87 ms, Average inference time: 7.03 ms

2025-08-28 13:47:50.370 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:47:50.447 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:47:50.525 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch293
2025-08-28 13:47:53.330 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 293/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.138s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 2.5, cls_loss: 0.6, lr: 1.041e-03, size: 448, ETA: 1:43:21
2025-08-28 13:47:56.261 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 293/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.6, lr: 1.040e-03, size: 416, ETA: 1:43:18
2025-08-28 13:47:59.288 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 293/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 1.039e-03, size: 384, ETA: 1:43:15
2025-08-28 13:48:02.286 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 293/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 1.039e-03, size: 288, ETA: 1:43:11
2025-08-28 13:48:05.199 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 293/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 0.7, lr: 1.038e-03, size: 512, ETA: 1:43:08
2025-08-28 13:48:08.256 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 293/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 1.037e-03, size: 384, ETA: 1:43:05
2025-08-28 13:48:09.601 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:48:15.790 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:48:16.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:48:16.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5428
2025-08-28 13:48:16.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4921
2025-08-28 13:48:16.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3235
2025-08-28 13:48:16.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4528
2025-08-28 13:48:16.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:48:16.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:48:16.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-08-28 13:48:16.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.492
2025-08-28 13:48:16.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.323
2025-08-28 13:48:16.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.453
2025-08-28 13:48:16.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:48:16.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:48:16.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:48:16.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:48:16.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:48:16.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:48:16.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:48:16.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:48:16.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:48:17.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:48:17.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:48:18.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:48:18.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:48:19.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:48:19.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:48:20.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:48:20.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:48:21.471 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:48:21.471 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 13:48:21.471 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 13:48:21.472 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:48:21.478 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.91 ms, Average inference time: 7.11 ms

2025-08-28 13:48:21.480 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:48:21.612 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:48:21.684 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch294
2025-08-28 13:48:24.653 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 294/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 1.2, lr: 1.036e-03, size: 480, ETA: 1:43:00
2025-08-28 13:48:27.632 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 294/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 8.1, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 8.1, cls_loss: 0.0, lr: 1.035e-03, size: 352, ETA: 1:42:57
2025-08-28 13:48:30.607 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 294/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.034e-03, size: 512, ETA: 1:42:53
2025-08-28 13:48:33.706 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 294/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.7, lr: 1.033e-03, size: 288, ETA: 1:42:50
2025-08-28 13:48:36.715 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 294/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 1.033e-03, size: 320, ETA: 1:42:47
2025-08-28 13:48:39.693 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 294/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.8Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.3, iou_loss: 1.6, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 1.3, lr: 1.032e-03, size: 512, ETA: 1:42:44
2025-08-28 13:48:41.026 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:48:47.094 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:48:47.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:48:48.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5513
2025-08-28 13:48:48.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5064
2025-08-28 13:48:48.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3345
2025-08-28 13:48:48.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4641
2025-08-28 13:48:48.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:48:48.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:48:48.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-08-28 13:48:48.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-08-28 13:48:48.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.335
2025-08-28 13:48:48.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.464
2025-08-28 13:48:48.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:48:48.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:48:48.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:48:48.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:48:48.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:48:48.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:48:48.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:48:48.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:48:48.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:48:48.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:48:49.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:48:50.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:48:50.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:48:51.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:48:51.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:48:52.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:48:52.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:48:53.159 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:48:53.159 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:48:53.159 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 13:48:53.159 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:48:53.166 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.88 ms, Average inference time: 7.08 ms

2025-08-28 13:48:53.167 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:48:53.249 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:48:53.330 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch295
2025-08-28 13:48:56.378 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 295/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.004s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.6, lr: 1.031e-03, size: 480, ETA: 1:42:39
2025-08-28 13:48:59.438 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 295/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.030e-03, size: 384, ETA: 1:42:36
2025-08-28 13:49:02.649 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 295/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.029e-03, size: 288, ETA: 1:42:32
2025-08-28 13:49:05.760 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 295/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.6, lr: 1.028e-03, size: 544, ETA: 1:42:29
2025-08-28 13:49:08.880 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 295/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 1.027e-03, size: 384, ETA: 1:42:26
2025-08-28 13:49:11.896 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 295/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.7, lr: 1.027e-03, size: 512, ETA: 1:42:23
2025-08-28 13:49:13.263 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:49:19.527 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:49:20.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:49:20.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5085
2025-08-28 13:49:20.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4821
2025-08-28 13:49:20.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2113
2025-08-28 13:49:20.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4007
2025-08-28 13:49:20.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:49:20.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:49:20.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-08-28 13:49:20.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-28 13:49:20.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.211
2025-08-28 13:49:20.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.401
2025-08-28 13:49:20.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:49:20.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:49:20.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:49:20.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:49:20.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:49:20.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:49:20.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:49:20.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:49:20.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:49:21.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:49:22.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:49:22.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:49:23.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:49:24.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:49:24.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:49:25.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:49:25.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:49:26.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:49:26.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 13:49:26.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-08-28 13:49:26.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:49:26.436 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.88 ms, Average inference time: 7.08 ms

2025-08-28 13:49:26.438 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:49:26.527 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:49:26.605 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch296
2025-08-28 13:49:29.525 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 296/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.5, lr: 1.025e-03, size: 480, ETA: 1:42:18
2025-08-28 13:49:32.487 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 296/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.025e-03, size: 512, ETA: 1:42:15
2025-08-28 13:49:35.616 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 296/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.5, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.5, lr: 1.024e-03, size: 384, ETA: 1:42:11
2025-08-28 13:49:38.743 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 296/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 1.023e-03, size: 256, ETA: 1:42:08
2025-08-28 13:49:41.720 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 296/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.7, lr: 1.022e-03, size: 416, ETA: 1:42:05
2025-08-28 13:49:44.741 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 296/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.7, cls_loss: 0.7, lr: 1.021e-03, size: 320, ETA: 1:42:02
2025-08-28 13:49:46.108 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:49:52.367 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:49:53.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:49:53.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5860
2025-08-28 13:49:54.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5351
2025-08-28 13:49:54.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3696
2025-08-28 13:49:54.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4969
2025-08-28 13:49:54.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:49:54.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:49:54.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-08-28 13:49:54.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-08-28 13:49:54.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-08-28 13:49:54.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-08-28 13:49:54.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:49:54.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:49:54.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:49:54.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:49:54.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:49:54.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:49:54.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:49:54.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:49:54.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:49:54.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:49:55.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:49:56.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:49:57.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:49:58.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:49:58.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:49:59.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:50:00.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:50:01.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:50:01.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:50:01.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 13:50:01.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:50:01.226 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.91 ms, Average inference time: 7.00 ms

2025-08-28 13:50:01.228 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:50:01.311 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:50:01.432 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch297
2025-08-28 13:50:04.454 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 297/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.020e-03, size: 576, ETA: 1:41:57
2025-08-28 13:50:07.447 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 297/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.8, lr: 1.019e-03, size: 352, ETA: 1:41:54
2025-08-28 13:50:10.384 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 297/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.8, lr: 1.019e-03, size: 448, ETA: 1:41:50
2025-08-28 13:50:13.437 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 297/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.018e-03, size: 288, ETA: 1:41:47
2025-08-28 13:50:16.396 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 297/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.6, lr: 1.017e-03, size: 256, ETA: 1:41:44
2025-08-28 13:50:19.314 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 297/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.8, lr: 1.016e-03, size: 320, ETA: 1:41:40
2025-08-28 13:50:20.650 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:50:26.696 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:50:27.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:50:27.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5526
2025-08-28 13:50:27.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4760
2025-08-28 13:50:27.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3724
2025-08-28 13:50:27.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4670
2025-08-28 13:50:27.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:50:27.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:50:27.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.553
2025-08-28 13:50:27.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-08-28 13:50:27.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-28 13:50:27.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.467
2025-08-28 13:50:27.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:50:27.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:50:27.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:50:27.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:50:27.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:50:27.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:50:27.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:50:27.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:50:27.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:50:28.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:50:28.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:50:28.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:50:29.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:50:29.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:50:29.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:50:30.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:50:30.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:50:31.138 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:50:31.138 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:50:31.138 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:50:31.138 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:50:31.144 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.87 ms, Average inference time: 7.00 ms

2025-08-28 13:50:31.146 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:50:31.226 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:50:31.312 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch298
2025-08-28 13:50:34.368 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 298/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.6, lr: 1.015e-03, size: 416, ETA: 1:41:36
2025-08-28 13:50:37.448 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 298/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 1.014e-03, size: 320, ETA: 1:41:32
2025-08-28 13:50:40.413 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 298/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 9.3, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 5.2, cls_loss: 0.8, lr: 1.013e-03, size: 448, ETA: 1:41:29
2025-08-28 13:50:43.384 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 298/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.003s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.012e-03, size: 480, ETA: 1:41:26
2025-08-28 13:50:46.458 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 298/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 1.012e-03, size: 352, ETA: 1:41:23
2025-08-28 13:50:49.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 298/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 1.011e-03, size: 416, ETA: 1:41:19
2025-08-28 13:50:50.813 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:50:56.921 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:50:57.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:50:58.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5839
2025-08-28 13:50:58.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4809
2025-08-28 13:50:58.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3705
2025-08-28 13:50:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4784
2025-08-28 13:50:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:50:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:50:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-08-28 13:50:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-28 13:50:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-08-28 13:50:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-08-28 13:50:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:50:58.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:50:58.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:50:58.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:50:58.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:50:58.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:50:58.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:50:58.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:50:58.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:50:58.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:50:59.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:50:59.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:51:00.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:51:01.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:51:01.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:51:02.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:51:02.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:51:03.389 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:51:03.389 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:51:03.389 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:51:03.389 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:51:03.396 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.90 ms, Average inference time: 7.07 ms

2025-08-28 13:51:03.397 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:51:03.500 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:51:03.573 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch299
2025-08-28 13:51:06.511 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 299/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 12.7, iou_loss: 4.2, l1_loss: 1.9, conf_loss: 5.9, cls_loss: 0.8, lr: 1.010e-03, size: 256, ETA: 1:41:14
2025-08-28 13:51:09.569 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 299/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.009e-03, size: 544, ETA: 1:41:11
2025-08-28 13:51:12.555 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 299/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 1.008e-03, size: 352, ETA: 1:41:08
2025-08-28 13:51:15.560 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 299/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.7, lr: 1.007e-03, size: 256, ETA: 1:41:05
2025-08-28 13:51:18.437 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 299/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.006e-03, size: 256, ETA: 1:41:01
2025-08-28 13:51:21.588 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 299/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.9, lr: 1.006e-03, size: 288, ETA: 1:40:58
2025-08-28 13:51:22.878 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:51:29.146 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:51:30.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:51:31.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5586
2025-08-28 13:51:31.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4399
2025-08-28 13:51:31.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3303
2025-08-28 13:51:31.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4429
2025-08-28 13:51:31.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:51:31.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:51:31.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-08-28 13:51:31.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.440
2025-08-28 13:51:31.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.330
2025-08-28 13:51:31.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.443
2025-08-28 13:51:31.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:51:31.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:51:31.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:51:31.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:51:31.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:51:31.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:51:31.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:51:31.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:51:31.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:51:32.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:51:33.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:51:34.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:51:35.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:51:36.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:51:37.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:51:38.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:51:39.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:51:39.990 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:51:39.991 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 13:51:39.991 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 13:51:39.991 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:51:39.998 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.95 ms, Average inference time: 7.13 ms

2025-08-28 13:51:39.999 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:51:40.078 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:51:40.161 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch300
2025-08-28 13:51:43.151 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 300/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.8, lr: 1.004e-03, size: 512, ETA: 1:40:53
2025-08-28 13:51:46.051 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 300/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 1.004e-03, size: 512, ETA: 1:40:50
2025-08-28 13:51:49.158 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 300/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 1.003e-03, size: 288, ETA: 1:40:47
2025-08-28 13:51:52.284 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 300/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 1.002e-03, size: 288, ETA: 1:40:43
2025-08-28 13:51:55.214 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 300/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.3, l1_loss: 1.4, conf_loss: 2.7, cls_loss: 1.0, lr: 1.001e-03, size: 384, ETA: 1:40:40
2025-08-28 13:51:58.284 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 300/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.9, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 3.1, cls_loss: 0.5, lr: 1.000e-03, size: 480, ETA: 1:40:37
2025-08-28 13:51:59.732 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:52:06.020 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:52:06.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:52:07.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5806
2025-08-28 13:52:07.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4777
2025-08-28 13:52:07.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3845
2025-08-28 13:52:07.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4809
2025-08-28 13:52:07.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:52:07.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:52:07.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.581
2025-08-28 13:52:07.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.478
2025-08-28 13:52:07.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-28 13:52:07.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-08-28 13:52:07.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:52:07.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:52:07.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:52:07.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:52:07.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:52:07.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:52:07.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:52:07.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:52:07.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:52:08.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:52:08.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:52:09.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:52:10.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:52:10.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:52:11.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:52:12.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:52:12.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:52:13.354 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:52:13.355 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:52:13.355 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:52:13.355 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:52:13.362 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.92 ms, Average inference time: 7.15 ms

2025-08-28 13:52:13.364 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:52:13.442 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:52:13.525 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch301
2025-08-28 13:52:16.382 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 301/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.5, lr: 9.992e-04, size: 320, ETA: 1:40:32
2025-08-28 13:52:19.354 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 301/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 9.984e-04, size: 384, ETA: 1:40:29
2025-08-28 13:52:22.360 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 301/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 1.0, lr: 9.976e-04, size: 416, ETA: 1:40:25
2025-08-28 13:52:25.428 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 301/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 9.968e-04, size: 512, ETA: 1:40:22
2025-08-28 13:52:28.585 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 301/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 9.959e-04, size: 480, ETA: 1:40:19
2025-08-28 13:52:31.523 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 301/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.5, lr: 9.951e-04, size: 384, ETA: 1:40:16
2025-08-28 13:52:32.974 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:52:39.030 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:52:40.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:52:40.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5840
2025-08-28 13:52:41.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5148
2025-08-28 13:52:41.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3652
2025-08-28 13:52:41.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4880
2025-08-28 13:52:41.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:52:41.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:52:41.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-08-28 13:52:41.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 13:52:41.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-08-28 13:52:41.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-08-28 13:52:41.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:52:41.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:52:41.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:52:41.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:52:41.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:52:41.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:52:41.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:52:41.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:52:41.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:52:42.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:52:43.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:52:43.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:52:44.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:52:45.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:52:46.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:52:47.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:52:48.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:52:49.540 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:52:49.540 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:52:49.540 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:52:49.541 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:52:49.548 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.91 ms, Average inference time: 7.19 ms

2025-08-28 13:52:49.549 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:52:49.681 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:52:49.754 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch302
2025-08-28 13:52:52.606 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 302/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 9.940e-04, size: 256, ETA: 1:40:11
2025-08-28 13:52:55.645 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 302/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.6, lr: 9.931e-04, size: 512, ETA: 1:40:08
2025-08-28 13:52:58.636 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 302/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.8, lr: 9.923e-04, size: 480, ETA: 1:40:04
2025-08-28 13:53:01.685 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 302/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 9.915e-04, size: 384, ETA: 1:40:01
2025-08-28 13:53:04.647 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 302/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 9.907e-04, size: 384, ETA: 1:39:58
2025-08-28 13:53:07.724 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 302/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.9, lr: 9.899e-04, size: 384, ETA: 1:39:55
2025-08-28 13:53:09.129 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:53:15.249 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:53:16.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:53:16.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5695
2025-08-28 13:53:16.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4838
2025-08-28 13:53:16.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3563
2025-08-28 13:53:16.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4699
2025-08-28 13:53:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:53:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:53:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.569
2025-08-28 13:53:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-28 13:53:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-08-28 13:53:16.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.470
2025-08-28 13:53:16.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:53:16.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:53:16.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:53:16.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:53:16.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:53:16.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:53:16.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:53:16.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:53:16.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:53:17.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:53:18.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:53:19.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:53:19.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:53:20.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:53:21.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:53:21.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:53:22.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:53:23.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:53:23.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:53:23.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:53:23.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:53:23.241 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.95 ms, Average inference time: 7.15 ms

2025-08-28 13:53:23.242 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:53:23.326 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:53:23.405 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch303
2025-08-28 13:53:26.270 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 303/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 1.0, lr: 9.887e-04, size: 448, ETA: 1:39:50
2025-08-28 13:53:29.335 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 303/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 9.879e-04, size: 256, ETA: 1:39:47
2025-08-28 13:53:32.470 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 303/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 9.5, iou_loss: 3.5, l1_loss: 1.7, conf_loss: 3.8, cls_loss: 0.6, lr: 9.871e-04, size: 320, ETA: 1:39:44
2025-08-28 13:53:35.641 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 303/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 9.863e-04, size: 512, ETA: 1:39:40
2025-08-28 13:53:38.641 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 303/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.6, lr: 9.855e-04, size: 416, ETA: 1:39:37
2025-08-28 13:53:41.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 303/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 9.847e-04, size: 512, ETA: 1:39:34
2025-08-28 13:53:43.009 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:53:49.250 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:53:50.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:53:51.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5716
2025-08-28 13:53:51.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4724
2025-08-28 13:53:51.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3664
2025-08-28 13:53:51.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4701
2025-08-28 13:53:51.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:53:51.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:53:51.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-08-28 13:53:51.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-08-28 13:53:51.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-08-28 13:53:51.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.470
2025-08-28 13:53:51.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:53:51.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:53:51.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:53:51.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:53:51.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:53:51.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:53:51.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:53:51.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:53:51.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:53:52.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:53:53.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:53:54.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:53:54.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:53:55.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:53:56.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:53:57.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:53:58.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:53:59.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:53:59.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:53:59.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:53:59.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:53:59.604 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.96 ms, Average inference time: 7.14 ms

2025-08-28 13:53:59.606 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:53:59.705 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:53:59.818 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch304
2025-08-28 13:54:02.603 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 304/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.137s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 2.2, cls_loss: 0.8, lr: 9.835e-04, size: 320, ETA: 1:39:29
2025-08-28 13:54:05.821 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 304/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 9.827e-04, size: 256, ETA: 1:39:26
2025-08-28 13:54:08.982 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 304/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 9.819e-04, size: 384, ETA: 1:39:23
2025-08-28 13:54:12.124 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 304/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 9.810e-04, size: 256, ETA: 1:39:20
2025-08-28 13:54:15.228 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 304/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 9.802e-04, size: 256, ETA: 1:39:16
2025-08-28 13:54:18.165 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 304/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 9.794e-04, size: 512, ETA: 1:39:13
2025-08-28 13:54:19.531 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:54:25.858 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:54:26.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:54:27.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5129
2025-08-28 13:54:27.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4729
2025-08-28 13:54:27.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3002
2025-08-28 13:54:27.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4287
2025-08-28 13:54:27.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:54:27.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:54:27.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-08-28 13:54:27.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.473
2025-08-28 13:54:27.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.300
2025-08-28 13:54:27.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.429
2025-08-28 13:54:27.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:54:27.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:54:27.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:54:27.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:54:27.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:54:27.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:54:27.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:54:27.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:54:27.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:54:28.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:54:28.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:54:29.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:54:30.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:54:30.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:54:31.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:54:32.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:54:32.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:54:33.652 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:54:33.652 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 13:54:33.652 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 13:54:33.652 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:54:33.660 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.95 ms, Average inference time: 7.19 ms

2025-08-28 13:54:33.661 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:54:33.745 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:54:33.824 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch305
2025-08-28 13:54:36.843 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 305/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 9.782e-04, size: 576, ETA: 1:39:08
2025-08-28 13:54:39.956 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 305/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 9.774e-04, size: 256, ETA: 1:39:05
2025-08-28 13:54:42.979 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 305/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 9.766e-04, size: 512, ETA: 1:39:02
2025-08-28 13:54:45.976 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 305/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 9.758e-04, size: 320, ETA: 1:38:59
2025-08-28 13:54:48.895 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 305/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 11.1, iou_loss: 3.3, l1_loss: 1.8, conf_loss: 5.2, cls_loss: 0.8, lr: 9.750e-04, size: 320, ETA: 1:38:55
2025-08-28 13:54:52.016 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 305/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.8, lr: 9.742e-04, size: 320, ETA: 1:38:52
2025-08-28 13:54:53.343 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:54:59.424 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:55:00.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:55:00.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5272
2025-08-28 13:55:00.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4252
2025-08-28 13:55:00.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3722
2025-08-28 13:55:00.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4416
2025-08-28 13:55:00.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:55:00.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:55:00.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-08-28 13:55:00.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-08-28 13:55:00.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-28 13:55:00.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.442
2025-08-28 13:55:00.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:55:00.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:55:00.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:55:00.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:55:00.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:55:00.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:55:00.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:55:00.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:55:00.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:55:01.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:55:01.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:55:02.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:55:02.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:55:03.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:55:04.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:55:04.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:55:05.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:55:05.718 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:55:05.719 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:55:05.719 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 13:55:05.719 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:55:05.726 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.95 ms, Average inference time: 7.21 ms

2025-08-28 13:55:05.727 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:55:05.814 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:55:05.939 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch306
2025-08-28 13:55:08.811 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 306/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.7, lr: 9.730e-04, size: 320, ETA: 1:38:47
2025-08-28 13:55:11.813 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 306/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.8, lr: 9.722e-04, size: 544, ETA: 1:38:44
2025-08-28 13:55:14.852 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 306/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 9.714e-04, size: 384, ETA: 1:38:41
2025-08-28 13:55:17.870 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 306/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 9.706e-04, size: 288, ETA: 1:38:37
2025-08-28 13:55:20.896 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 306/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 9.698e-04, size: 576, ETA: 1:38:34
2025-08-28 13:55:23.947 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 306/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.4, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.7, lr: 9.690e-04, size: 288, ETA: 1:38:31
2025-08-28 13:55:25.289 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:55:31.307 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:55:32.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:55:32.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5705
2025-08-28 13:55:33.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5024
2025-08-28 13:55:33.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3876
2025-08-28 13:55:33.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4868
2025-08-28 13:55:33.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:55:33.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:55:33.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.570
2025-08-28 13:55:33.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-08-28 13:55:33.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-08-28 13:55:33.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-08-28 13:55:33.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:55:33.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:55:33.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:55:33.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:55:33.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:55:33.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:55:33.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:55:33.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:55:33.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:55:33.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:55:34.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:55:35.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:55:36.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:55:37.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:55:37.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:55:38.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:55:39.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:55:40.325 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:55:40.325 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:55:40.326 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:55:40.326 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:55:40.333 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.92 ms, Average inference time: 7.15 ms

2025-08-28 13:55:40.334 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:55:40.465 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:55:40.537 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch307
2025-08-28 13:55:43.487 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 307/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 9.678e-04, size: 576, ETA: 1:38:26
2025-08-28 13:55:46.522 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 307/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.8, lr: 9.670e-04, size: 352, ETA: 1:38:23
2025-08-28 13:55:49.460 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 307/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 9.662e-04, size: 480, ETA: 1:38:20
2025-08-28 13:55:52.630 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 307/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.5, lr: 9.653e-04, size: 576, ETA: 1:38:17
2025-08-28 13:55:55.717 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 307/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 2.2, cls_loss: 0.8, lr: 9.645e-04, size: 256, ETA: 1:38:13
2025-08-28 13:55:58.790 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 307/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.8, lr: 9.637e-04, size: 576, ETA: 1:38:10
2025-08-28 13:56:00.234 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:56:06.430 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:56:07.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:56:08.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5392
2025-08-28 13:56:08.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4677
2025-08-28 13:56:08.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3498
2025-08-28 13:56:08.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4522
2025-08-28 13:56:08.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:56:08.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:56:08.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-08-28 13:56:08.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-08-28 13:56:08.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.350
2025-08-28 13:56:08.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.452
2025-08-28 13:56:08.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:56:08.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:56:08.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:56:08.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:56:08.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:56:08.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:56:08.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:56:08.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:56:08.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:56:09.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:56:10.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:56:11.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:56:12.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:56:13.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:56:13.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:56:14.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:56:15.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:56:16.644 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:56:16.644 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-08-28 13:56:16.644 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 13:56:16.644 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:56:16.651 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.96 ms, Average inference time: 7.21 ms

2025-08-28 13:56:16.652 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:56:16.734 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:56:16.817 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch308
2025-08-28 13:56:19.909 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 308/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.8, lr: 9.625e-04, size: 416, ETA: 1:38:06
2025-08-28 13:56:22.926 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 308/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.7, lr: 9.617e-04, size: 256, ETA: 1:38:02
2025-08-28 13:56:25.973 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 308/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 9.609e-04, size: 352, ETA: 1:37:59
2025-08-28 13:56:29.090 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 308/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 9.601e-04, size: 512, ETA: 1:37:56
2025-08-28 13:56:32.126 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 308/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 9.593e-04, size: 384, ETA: 1:37:53
2025-08-28 13:56:35.125 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 308/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 9.585e-04, size: 512, ETA: 1:37:49
2025-08-28 13:56:36.504 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:56:42.647 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:56:43.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:56:43.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5767
2025-08-28 13:56:43.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5210
2025-08-28 13:56:43.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3605
2025-08-28 13:56:43.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4861
2025-08-28 13:56:43.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:56:43.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:56:43.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.577
2025-08-28 13:56:43.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-08-28 13:56:43.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.360
2025-08-28 13:56:43.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.486
2025-08-28 13:56:43.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:56:43.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:56:43.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:56:43.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:56:43.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:56:43.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:56:43.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:56:43.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:56:43.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:56:44.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:56:45.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:56:45.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:56:46.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:56:47.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:56:47.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:56:48.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:56:48.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:56:49.368 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:56:49.368 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 13:56:49.368 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 13:56:49.368 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:56:49.375 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.91 ms, Average inference time: 7.18 ms

2025-08-28 13:56:49.376 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:56:49.452 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:56:49.532 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch309
2025-08-28 13:56:52.468 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 309/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.8, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 7.8, cls_loss: 0.0, lr: 9.573e-04, size: 384, ETA: 1:37:45
2025-08-28 13:56:55.471 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 309/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.3, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 0.7, lr: 9.565e-04, size: 512, ETA: 1:37:41
2025-08-28 13:56:58.542 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 309/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.9, lr: 9.557e-04, size: 288, ETA: 1:37:38
2025-08-28 13:57:01.470 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 309/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.003s, total_loss: 7.9, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.4, cls_loss: 0.8, lr: 9.549e-04, size: 576, ETA: 1:37:35
2025-08-28 13:57:04.602 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 309/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 9.541e-04, size: 448, ETA: 1:37:32
2025-08-28 13:57:07.617 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 309/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.8, lr: 9.533e-04, size: 416, ETA: 1:37:28
2025-08-28 13:57:08.924 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:57:15.157 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:57:15.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:57:16.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5768
2025-08-28 13:57:16.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4654
2025-08-28 13:57:16.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3684
2025-08-28 13:57:16.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4702
2025-08-28 13:57:16.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:57:16.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:57:16.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.577
2025-08-28 13:57:16.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.465
2025-08-28 13:57:16.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-28 13:57:16.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.470
2025-08-28 13:57:16.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:57:16.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:57:16.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:57:16.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:57:16.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:57:16.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:57:16.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:57:16.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:57:16.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:57:17.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:57:17.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:57:18.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:57:18.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:57:19.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:57:20.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:57:20.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:57:21.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:57:22.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:57:22.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:57:22.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:57:22.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:57:22.040 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.92 ms, Average inference time: 7.22 ms

2025-08-28 13:57:22.041 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:57:22.127 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:57:22.207 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch310
2025-08-28 13:57:25.046 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 310/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 9.521e-04, size: 448, ETA: 1:37:24
2025-08-28 13:57:28.172 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 310/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 9.513e-04, size: 480, ETA: 1:37:20
2025-08-28 13:57:31.221 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 310/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 9.505e-04, size: 320, ETA: 1:37:17
2025-08-28 13:57:34.368 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 310/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.7, lr: 9.497e-04, size: 288, ETA: 1:37:14
2025-08-28 13:57:37.457 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 310/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 9.488e-04, size: 256, ETA: 1:37:11
2025-08-28 13:57:40.388 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 310/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.8, lr: 9.480e-04, size: 576, ETA: 1:37:08
2025-08-28 13:57:41.863 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:57:48.035 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:57:48.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:57:49.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5839
2025-08-28 13:57:49.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4676
2025-08-28 13:57:49.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3517
2025-08-28 13:57:49.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4677
2025-08-28 13:57:49.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:57:49.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:57:49.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-08-28 13:57:49.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-08-28 13:57:49.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.352
2025-08-28 13:57:49.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.468
2025-08-28 13:57:49.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:57:49.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:57:49.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:57:49.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:57:49.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:57:49.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:57:49.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:57:49.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:57:49.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:57:49.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:57:50.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:57:50.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:57:51.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:57:51.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:57:52.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:57:53.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:57:53.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:57:54.092 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:57:54.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:57:54.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:57:54.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:57:54.100 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.92 ms, Average inference time: 7.11 ms

2025-08-28 13:57:54.100 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:57:54.227 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:57:54.299 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch311
2025-08-28 13:57:57.200 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 311/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 9.469e-04, size: 480, ETA: 1:37:03
2025-08-28 13:58:00.186 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 311/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.7, lr: 9.460e-04, size: 352, ETA: 1:37:00
2025-08-28 13:58:03.338 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 311/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.9, lr: 9.452e-04, size: 288, ETA: 1:36:56
2025-08-28 13:58:06.596 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 311/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.7, lr: 9.444e-04, size: 256, ETA: 1:36:53
2025-08-28 13:58:09.543 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 311/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 9.436e-04, size: 352, ETA: 1:36:50
2025-08-28 13:58:12.557 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 311/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 9.428e-04, size: 544, ETA: 1:36:47
2025-08-28 13:58:13.942 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:58:20.158 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:58:21.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:58:22.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5815
2025-08-28 13:58:22.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4991
2025-08-28 13:58:22.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3564
2025-08-28 13:58:22.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4790
2025-08-28 13:58:22.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:58:22.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:58:22.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-08-28 13:58:22.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-08-28 13:58:22.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-08-28 13:58:22.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.479
2025-08-28 13:58:22.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:58:22.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:58:22.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:58:22.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:58:22.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:58:22.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:58:22.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:58:22.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:58:22.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:58:23.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:58:24.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:58:25.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:58:26.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:58:27.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:58:28.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:58:29.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:58:30.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:58:31.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:58:31.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 13:58:31.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:58:31.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:58:31.379 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.93 ms, Average inference time: 7.04 ms

2025-08-28 13:58:31.380 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:58:31.463 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:58:31.546 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch312
2025-08-28 13:58:34.356 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 312/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 9.416e-04, size: 416, ETA: 1:36:42
2025-08-28 13:58:37.507 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 312/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 9.408e-04, size: 288, ETA: 1:36:39
2025-08-28 13:58:40.509 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 312/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 9.400e-04, size: 480, ETA: 1:36:36
2025-08-28 13:58:43.533 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 312/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 9.392e-04, size: 544, ETA: 1:36:32
2025-08-28 13:58:46.510 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 312/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 9.384e-04, size: 320, ETA: 1:36:29
2025-08-28 13:58:49.592 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 312/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 3.0, cls_loss: 0.7, lr: 9.376e-04, size: 576, ETA: 1:36:26
2025-08-28 13:58:50.936 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:58:57.168 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:58:57.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:58:58.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5862
2025-08-28 13:58:58.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5067
2025-08-28 13:58:58.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3422
2025-08-28 13:58:58.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4784
2025-08-28 13:58:58.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:58:58.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:58:58.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-08-28 13:58:58.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-08-28 13:58:58.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.342
2025-08-28 13:58:58.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-08-28 13:58:58.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:58:58.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:58:58.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:58:58.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:58:58.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:58:58.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:58:58.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:58:58.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:58:58.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:58:59.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:58:59.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:59:00.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:59:01.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:59:02.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:59:02.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:59:03.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:59:03.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:59:04.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:59:04.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 13:59:04.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 13:59:04.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:59:04.604 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.96 ms, Average inference time: 7.24 ms

2025-08-28 13:59:04.606 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:59:04.690 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:59:04.770 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch313
2025-08-28 13:59:07.679 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 313/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.9, lr: 9.364e-04, size: 576, ETA: 1:36:21
2025-08-28 13:59:10.797 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 313/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 1.1, lr: 9.356e-04, size: 256, ETA: 1:36:18
2025-08-28 13:59:13.773 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 313/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.8, lr: 9.348e-04, size: 320, ETA: 1:36:15
2025-08-28 13:59:16.756 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 313/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.003s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 9.340e-04, size: 352, ETA: 1:36:11
2025-08-28 13:59:19.824 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 313/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.3, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 1.0, lr: 9.332e-04, size: 512, ETA: 1:36:08
2025-08-28 13:59:22.901 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 313/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 9.323e-04, size: 352, ETA: 1:36:05
2025-08-28 13:59:24.304 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:59:30.357 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 13:59:30.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 13:59:31.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5627
2025-08-28 13:59:31.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4772
2025-08-28 13:59:31.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3684
2025-08-28 13:59:31.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4694
2025-08-28 13:59:31.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 13:59:31.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 13:59:31.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.563
2025-08-28 13:59:31.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.477
2025-08-28 13:59:31.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-28 13:59:31.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.469
2025-08-28 13:59:31.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 13:59:31.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 13:59:31.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 13:59:31.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 13:59:31.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 13:59:31.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 13:59:31.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 13:59:31.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 13:59:31.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 13:59:31.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 13:59:32.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 13:59:32.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 13:59:32.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 13:59:33.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 13:59:33.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 13:59:34.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 13:59:34.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 13:59:34.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 13:59:34.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 13:59:34.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 13:59:34.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 13:59:34.965 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.87 ms, Average inference time: 7.11 ms

2025-08-28 13:59:34.966 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:59:35.105 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 13:59:35.175 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch314
2025-08-28 13:59:38.139 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 314/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 0.8, cls_loss: 0.6, lr: 9.312e-04, size: 384, ETA: 1:36:00
2025-08-28 13:59:41.112 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 314/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.8, lr: 9.304e-04, size: 544, ETA: 1:35:57
2025-08-28 13:59:44.218 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 314/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.8, lr: 9.296e-04, size: 544, ETA: 1:35:54
2025-08-28 13:59:47.325 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 314/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 8.6, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.7, cls_loss: 0.8, lr: 9.287e-04, size: 480, ETA: 1:35:51
2025-08-28 13:59:50.346 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 314/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 9.279e-04, size: 320, ETA: 1:35:47
2025-08-28 13:59:53.350 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 314/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 1.0, lr: 9.271e-04, size: 512, ETA: 1:35:44
2025-08-28 13:59:54.683 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:00:00.862 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:00:01.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:00:02.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5899
2025-08-28 14:00:02.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4982
2025-08-28 14:00:02.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3950
2025-08-28 14:00:02.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4944
2025-08-28 14:00:02.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:00:02.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:00:02.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.590
2025-08-28 14:00:02.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-28 14:00:02.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-08-28 14:00:02.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-08-28 14:00:02.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:00:02.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:00:02.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:00:02.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:00:02.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:00:02.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:00:02.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:00:02.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:00:02.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:00:02.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:00:03.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:00:04.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:00:04.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:00:05.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:00:06.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:00:06.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:00:07.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:00:07.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:00:07.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:00:07.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:00:07.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:00:07.987 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.90 ms, Average inference time: 7.17 ms

2025-08-28 14:00:07.988 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:00:08.074 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:00:08.153 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch315
2025-08-28 14:00:11.056 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 315/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 9.260e-04, size: 384, ETA: 1:35:39
2025-08-28 14:00:14.214 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 315/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 9.251e-04, size: 384, ETA: 1:35:36
2025-08-28 14:00:17.201 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 315/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 9.243e-04, size: 544, ETA: 1:35:33
2025-08-28 14:00:20.265 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 315/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 3.1, cls_loss: 0.6, lr: 9.235e-04, size: 384, ETA: 1:35:30
2025-08-28 14:00:23.201 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 315/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 9.4, iou_loss: 3.3, l1_loss: 1.3, conf_loss: 4.2, cls_loss: 0.7, lr: 9.227e-04, size: 288, ETA: 1:35:26
2025-08-28 14:00:26.183 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 315/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.3, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 9.219e-04, size: 480, ETA: 1:35:23
2025-08-28 14:00:27.565 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:00:33.761 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:00:35.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:00:35.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5756
2025-08-28 14:00:36.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4868
2025-08-28 14:00:36.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3402
2025-08-28 14:00:36.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4675
2025-08-28 14:00:36.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:00:36.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:00:36.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-08-28 14:00:36.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-08-28 14:00:36.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-08-28 14:00:36.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.468
2025-08-28 14:00:36.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:00:36.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:00:36.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:00:36.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:00:36.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:00:36.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:00:36.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:00:36.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:00:36.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:00:37.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:00:38.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:00:39.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:00:40.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:00:41.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:00:42.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:00:43.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:00:44.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:00:45.610 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:00:45.611 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 14:00:45.611 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:00:45.611 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:00:45.618 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.07 ms, Average NMS time: 0.95 ms, Average inference time: 7.02 ms

2025-08-28 14:00:45.620 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:00:45.759 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:00:45.832 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch316
2025-08-28 14:00:48.769 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 316/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 9.207e-04, size: 256, ETA: 1:35:18
2025-08-28 14:00:51.909 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 316/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.8, lr: 9.199e-04, size: 288, ETA: 1:35:15
2025-08-28 14:00:55.038 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 316/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 9.191e-04, size: 384, ETA: 1:35:12
2025-08-28 14:00:58.083 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 316/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 9.183e-04, size: 544, ETA: 1:35:09
2025-08-28 14:01:01.080 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 316/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 9.175e-04, size: 416, ETA: 1:35:06
2025-08-28 14:01:04.096 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 316/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.5, lr: 9.167e-04, size: 384, ETA: 1:35:02
2025-08-28 14:01:05.491 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:01:11.577 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:01:12.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:01:13.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5763
2025-08-28 14:01:13.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5059
2025-08-28 14:01:13.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3394
2025-08-28 14:01:13.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4739
2025-08-28 14:01:13.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:01:13.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:01:13.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-08-28 14:01:13.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-08-28 14:01:13.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.339
2025-08-28 14:01:13.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.474
2025-08-28 14:01:13.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:01:13.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:01:13.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:01:13.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:01:13.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:01:13.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:01:13.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:01:13.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:01:13.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:01:14.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:01:15.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:01:15.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:01:16.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:01:17.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:01:18.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:01:19.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:01:20.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:01:21.139 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:01:21.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 14:01:21.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:01:21.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:01:21.147 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.92 ms, Average inference time: 7.13 ms

2025-08-28 14:01:21.162 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:01:21.242 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:01:21.322 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch317
2025-08-28 14:01:24.148 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 317/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 9.155e-04, size: 384, ETA: 1:34:58
2025-08-28 14:01:27.145 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 317/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 9.147e-04, size: 512, ETA: 1:34:54
2025-08-28 14:01:30.131 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 317/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.9, lr: 9.139e-04, size: 320, ETA: 1:34:51
2025-08-28 14:01:33.144 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 317/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 9.131e-04, size: 416, ETA: 1:34:48
2025-08-28 14:01:36.228 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 317/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 9.1, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 4.2, cls_loss: 0.9, lr: 9.123e-04, size: 544, ETA: 1:34:45
2025-08-28 14:01:39.365 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 317/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 9.115e-04, size: 384, ETA: 1:34:42
2025-08-28 14:01:40.712 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:01:47.053 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:01:47.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:01:48.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5500
2025-08-28 14:01:48.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4594
2025-08-28 14:01:48.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3400
2025-08-28 14:01:48.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4498
2025-08-28 14:01:48.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:01:48.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:01:48.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.550
2025-08-28 14:01:48.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.459
2025-08-28 14:01:48.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-08-28 14:01:48.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.450
2025-08-28 14:01:48.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:01:48.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:01:48.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:01:48.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:01:48.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:01:48.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:01:48.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:01:48.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:01:48.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:01:49.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:01:50.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:01:50.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:01:51.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:01:52.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:01:52.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:01:53.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:01:54.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:01:54.900 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:01:54.900 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 14:01:54.900 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 14:01:54.900 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:01:54.907 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.90 ms, Average inference time: 7.11 ms

2025-08-28 14:01:54.908 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:01:54.987 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:01:55.069 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch318
2025-08-28 14:01:57.849 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 318/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.137s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.2, cls_loss: 0.8, lr: 9.103e-04, size: 576, ETA: 1:34:37
2025-08-28 14:02:00.921 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 318/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.6, lr: 9.095e-04, size: 288, ETA: 1:34:33
2025-08-28 14:02:03.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 318/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 9.087e-04, size: 320, ETA: 1:34:30
2025-08-28 14:02:06.893 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 318/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.1, cls_loss: 0.6, lr: 9.079e-04, size: 544, ETA: 1:34:27
2025-08-28 14:02:09.964 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 318/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.6, lr: 9.071e-04, size: 576, ETA: 1:34:24
2025-08-28 14:02:13.016 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 318/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.7, lr: 9.063e-04, size: 288, ETA: 1:34:20
2025-08-28 14:02:14.407 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:02:20.530 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:02:21.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:02:22.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5856
2025-08-28 14:02:22.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5196
2025-08-28 14:02:22.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4022
2025-08-28 14:02:22.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5025
2025-08-28 14:02:22.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:02:22.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:02:22.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-08-28 14:02:22.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-08-28 14:02:22.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-28 14:02:22.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-08-28 14:02:22.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:02:22.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:02:22.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:02:22.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:02:22.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:02:22.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:02:22.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:02:22.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:02:22.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:02:23.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:02:24.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:02:24.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:02:25.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:02:26.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:02:27.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:02:28.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:02:28.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:02:29.816 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:02:29.816 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:02:29.816 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 14:02:29.816 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:02:29.823 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.91 ms, Average inference time: 7.07 ms

2025-08-28 14:02:29.825 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:02:29.943 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:02:30.017 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch319
2025-08-28 14:02:32.900 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 319/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 4.2, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.6, lr: 9.051e-04, size: 256, ETA: 1:34:16
2025-08-28 14:02:35.897 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 319/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.5, lr: 9.043e-04, size: 352, ETA: 1:34:12
2025-08-28 14:02:38.937 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 319/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 9.035e-04, size: 480, ETA: 1:34:09
2025-08-28 14:02:41.927 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 319/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 10.8, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 6.1, cls_loss: 0.9, lr: 9.027e-04, size: 320, ETA: 1:34:06
2025-08-28 14:02:44.866 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 319/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.5, lr: 9.019e-04, size: 544, ETA: 1:34:03
2025-08-28 14:02:47.902 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 319/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.8, lr: 9.010e-04, size: 544, ETA: 1:33:59
2025-08-28 14:02:49.288 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:02:55.470 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:02:56.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:02:56.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5747
2025-08-28 14:02:56.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4740
2025-08-28 14:02:56.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3592
2025-08-28 14:02:56.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4693
2025-08-28 14:02:56.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:02:56.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:02:56.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.575
2025-08-28 14:02:56.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-28 14:02:56.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-08-28 14:02:56.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.469
2025-08-28 14:02:56.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:02:56.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:02:56.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:02:56.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:02:56.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:02:56.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:02:56.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:02:56.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:02:56.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:02:57.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:02:57.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:02:58.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:02:58.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:02:59.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:02:59.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:03:00.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:03:00.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:03:01.209 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:03:01.209 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 14:03:01.209 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:03:01.209 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:03:01.217 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.93 ms, Average inference time: 7.15 ms

2025-08-28 14:03:01.218 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:03:01.350 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:03:01.422 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch320
2025-08-28 14:03:04.287 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 320/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 1.0, lr: 8.999e-04, size: 320, ETA: 1:33:55
2025-08-28 14:03:07.345 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 320/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.7, lr: 8.991e-04, size: 512, ETA: 1:33:51
2025-08-28 14:03:10.325 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 320/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 0.6, lr: 8.983e-04, size: 576, ETA: 1:33:48
2025-08-28 14:03:13.491 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 320/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 8.974e-04, size: 384, ETA: 1:33:45
2025-08-28 14:03:16.518 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 320/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.3, l1_loss: 1.1, conf_loss: 3.4, cls_loss: 0.8, lr: 8.966e-04, size: 288, ETA: 1:33:42
2025-08-28 14:03:19.451 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 320/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.6, lr: 8.958e-04, size: 256, ETA: 1:33:39
2025-08-28 14:03:20.771 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:03:26.813 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:03:27.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:03:28.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5808
2025-08-28 14:03:28.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4993
2025-08-28 14:03:28.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3829
2025-08-28 14:03:28.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4877
2025-08-28 14:03:28.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:03:28.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:03:28.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.581
2025-08-28 14:03:28.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-08-28 14:03:28.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-28 14:03:28.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-08-28 14:03:28.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:03:28.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:03:28.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:03:28.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:03:28.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:03:28.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:03:28.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:03:28.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:03:28.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:03:29.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:03:30.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:03:31.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:03:32.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:03:33.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:03:34.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:03:35.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:03:36.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:03:37.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:03:37.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:03:37.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:03:37.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:03:37.353 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.92 ms, Average inference time: 7.10 ms

2025-08-28 14:03:37.355 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:03:37.434 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:03:37.514 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch321
2025-08-28 14:03:40.392 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 321/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 3.9, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.6, lr: 8.947e-04, size: 416, ETA: 1:33:34
2025-08-28 14:03:43.458 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 321/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 8.939e-04, size: 544, ETA: 1:33:31
2025-08-28 14:03:46.451 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 321/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 8.930e-04, size: 352, ETA: 1:33:27
2025-08-28 14:03:49.576 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 321/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 8.922e-04, size: 544, ETA: 1:33:24
2025-08-28 14:03:52.557 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 321/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 8.914e-04, size: 512, ETA: 1:33:21
2025-08-28 14:03:55.647 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 321/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.5, lr: 8.906e-04, size: 480, ETA: 1:33:18
2025-08-28 14:03:56.996 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:04:03.181 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:04:03.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:04:04.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5897
2025-08-28 14:04:04.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4904
2025-08-28 14:04:04.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3631
2025-08-28 14:04:04.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4811
2025-08-28 14:04:04.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:04:04.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:04:04.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.590
2025-08-28 14:04:04.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-28 14:04:04.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-08-28 14:04:04.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-08-28 14:04:04.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:04:04.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:04:04.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:04:04.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:04:04.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:04:04.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:04:04.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:04:04.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:04:04.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:04:05.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:04:05.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:04:06.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:04:07.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:04:07.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:04:08.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:04:09.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:04:09.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:04:10.341 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:04:10.341 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:04:10.341 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:04:10.341 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:04:10.348 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.90 ms, Average inference time: 7.16 ms

2025-08-28 14:04:10.349 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:04:10.437 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:04:10.519 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch322
2025-08-28 14:04:13.445 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 322/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.7, lr: 8.895e-04, size: 288, ETA: 1:33:13
2025-08-28 14:04:16.482 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 322/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 8.887e-04, size: 384, ETA: 1:33:10
2025-08-28 14:04:19.399 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 322/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.6, lr: 8.878e-04, size: 320, ETA: 1:33:06
2025-08-28 14:04:22.398 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 322/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 8.870e-04, size: 384, ETA: 1:33:03
2025-08-28 14:04:25.567 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 322/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 8.862e-04, size: 576, ETA: 1:33:00
2025-08-28 14:04:28.628 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 322/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 3.7, cls_loss: 0.8, lr: 8.854e-04, size: 448, ETA: 1:32:57
2025-08-28 14:04:30.001 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:04:36.199 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:04:36.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:04:37.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5858
2025-08-28 14:04:37.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5146
2025-08-28 14:04:37.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3738
2025-08-28 14:04:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4914
2025-08-28 14:04:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:04:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:04:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-08-28 14:04:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 14:04:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.374
2025-08-28 14:04:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.491
2025-08-28 14:04:37.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:04:37.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:04:37.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:04:37.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:04:37.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:04:37.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:04:37.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:04:37.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:04:37.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:04:38.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:04:38.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:04:39.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:04:40.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:04:40.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:04:41.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:04:42.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:04:42.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:04:43.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:04:43.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:04:43.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:04:43.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:04:43.233 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.88 ms, Average inference time: 7.02 ms

2025-08-28 14:04:43.234 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:04:43.346 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:04:43.482 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch323
2025-08-28 14:04:46.314 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 323/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.5, lr: 8.843e-04, size: 352, ETA: 1:32:52
2025-08-28 14:04:49.343 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 323/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.3, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 8.835e-04, size: 352, ETA: 1:32:49
2025-08-28 14:04:52.282 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 323/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 8.826e-04, size: 416, ETA: 1:32:46
2025-08-28 14:04:55.368 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 323/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 8.818e-04, size: 512, ETA: 1:32:42
2025-08-28 14:04:58.346 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 323/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 0.7, lr: 8.810e-04, size: 384, ETA: 1:32:39
2025-08-28 14:05:01.306 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 323/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 8.802e-04, size: 448, ETA: 1:32:36
2025-08-28 14:05:02.615 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:05:08.744 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:05:09.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:05:10.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5785
2025-08-28 14:05:10.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4909
2025-08-28 14:05:10.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3232
2025-08-28 14:05:10.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4642
2025-08-28 14:05:10.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:05:10.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:05:10.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-08-28 14:05:10.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-08-28 14:05:10.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.323
2025-08-28 14:05:10.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.464
2025-08-28 14:05:10.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:05:10.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:05:10.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:05:10.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:05:10.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:05:10.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:05:10.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:05:10.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:05:10.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:05:10.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:05:11.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:05:12.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:05:12.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:05:13.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:05:14.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:05:14.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:05:15.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:05:16.228 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:05:16.229 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 14:05:16.229 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 14:05:16.229 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:05:16.236 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.93 ms, Average inference time: 7.15 ms

2025-08-28 14:05:16.237 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:05:16.397 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:05:16.468 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch324
2025-08-28 14:05:19.323 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 324/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 8.791e-04, size: 480, ETA: 1:32:31
2025-08-28 14:05:22.346 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 324/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 8.783e-04, size: 544, ETA: 1:32:28
2025-08-28 14:05:25.345 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 324/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 8.774e-04, size: 384, ETA: 1:32:25
2025-08-28 14:05:28.315 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 324/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 3.2, cls_loss: 0.8, lr: 8.766e-04, size: 320, ETA: 1:32:21
2025-08-28 14:05:31.188 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 324/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 8.5, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 3.3, cls_loss: 0.7, lr: 8.758e-04, size: 256, ETA: 1:32:18
2025-08-28 14:05:34.106 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 324/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.5, lr: 8.750e-04, size: 288, ETA: 1:32:15
2025-08-28 14:05:35.451 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:05:41.472 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:05:42.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:05:42.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5742
2025-08-28 14:05:42.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5035
2025-08-28 14:05:42.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3986
2025-08-28 14:05:42.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4921
2025-08-28 14:05:42.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:05:42.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:05:42.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-08-28 14:05:42.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-08-28 14:05:42.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-08-28 14:05:42.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-08-28 14:05:42.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:05:42.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:05:42.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:05:42.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:05:42.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:05:42.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:05:42.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:05:42.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:05:42.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:05:43.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:05:43.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:05:44.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:05:45.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:05:45.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:05:46.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:05:46.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:05:47.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:05:48.022 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:05:48.022 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:05:48.023 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:05:48.023 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:05:48.030 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.88 ms, Average inference time: 7.15 ms

2025-08-28 14:05:48.031 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:05:48.109 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:05:48.188 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch325
2025-08-28 14:05:51.091 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 325/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 8.739e-04, size: 320, ETA: 1:32:10
2025-08-28 14:05:54.272 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 325/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 8.731e-04, size: 288, ETA: 1:32:07
2025-08-28 14:05:57.234 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 325/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 8.723e-04, size: 320, ETA: 1:32:03
2025-08-28 14:06:00.266 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 325/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.7, lr: 8.714e-04, size: 256, ETA: 1:32:00
2025-08-28 14:06:03.289 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 325/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 8.706e-04, size: 576, ETA: 1:31:57
2025-08-28 14:06:06.453 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 325/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.8, l1_loss: 1.4, conf_loss: 3.6, cls_loss: 0.7, lr: 8.698e-04, size: 512, ETA: 1:31:54
2025-08-28 14:06:07.837 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:06:14.108 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:06:14.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:06:15.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5688
2025-08-28 14:06:15.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4846
2025-08-28 14:06:15.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3839
2025-08-28 14:06:15.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4791
2025-08-28 14:06:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:06:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:06:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.569
2025-08-28 14:06:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-08-28 14:06:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-08-28 14:06:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.479
2025-08-28 14:06:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:06:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:06:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:06:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:06:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:06:15.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:06:15.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:06:15.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:06:15.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:06:15.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:06:16.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:06:17.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:06:17.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:06:18.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:06:18.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:06:19.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:06:20.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:06:20.634 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:06:20.634 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:06:20.634 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:06:20.634 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:06:20.642 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.10 ms, Average NMS time: 0.94 ms, Average inference time: 7.04 ms

2025-08-28 14:06:20.642 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:06:20.724 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:06:20.806 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch326
2025-08-28 14:06:23.639 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 326/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 8.687e-04, size: 512, ETA: 1:31:49
2025-08-28 14:06:26.622 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 326/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.9, lr: 8.679e-04, size: 256, ETA: 1:31:46
2025-08-28 14:06:29.682 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 326/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.006s, total_loss: 6.2, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 3.0, cls_loss: 0.5, lr: 8.671e-04, size: 512, ETA: 1:31:43
2025-08-28 14:06:32.727 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 326/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 8.663e-04, size: 512, ETA: 1:31:39
2025-08-28 14:06:35.721 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 326/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 8.655e-04, size: 256, ETA: 1:31:36
2025-08-28 14:06:38.663 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 326/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.8, iou_loss: 1.9, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.5, lr: 8.646e-04, size: 544, ETA: 1:31:33
2025-08-28 14:06:40.072 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:06:46.222 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:06:46.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:06:47.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5776
2025-08-28 14:06:47.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5252
2025-08-28 14:06:47.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3999
2025-08-28 14:06:47.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5009
2025-08-28 14:06:47.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:06:47.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:06:47.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-08-28 14:06:47.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 14:06:47.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-28 14:06:47.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-08-28 14:06:47.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:06:47.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:06:47.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:06:47.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:06:47.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:06:47.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:06:47.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:06:47.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:06:47.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:06:48.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:06:48.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:06:49.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:06:50.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:06:50.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:06:51.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:06:51.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:06:52.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:06:53.133 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:06:53.133 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:06:53.133 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 14:06:53.133 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:06:53.140 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.91 ms, Average inference time: 7.10 ms

2025-08-28 14:06:53.141 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:06:53.223 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:06:53.304 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch327
2025-08-28 14:06:56.190 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 327/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 1.8, cls_loss: 0.5, lr: 8.635e-04, size: 384, ETA: 1:31:28
2025-08-28 14:06:59.319 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 327/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.8, lr: 8.627e-04, size: 576, ETA: 1:31:25
2025-08-28 14:07:02.406 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 327/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 8.619e-04, size: 352, ETA: 1:31:22
2025-08-28 14:07:05.364 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 327/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.5, lr: 8.611e-04, size: 384, ETA: 1:31:19
2025-08-28 14:07:08.347 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 327/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 8.603e-04, size: 288, ETA: 1:31:15
2025-08-28 14:07:11.447 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 327/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 8.595e-04, size: 320, ETA: 1:31:12
2025-08-28 14:07:12.786 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:07:19.045 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:07:19.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:07:20.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5761
2025-08-28 14:07:20.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4979
2025-08-28 14:07:20.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3765
2025-08-28 14:07:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4835
2025-08-28 14:07:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:07:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:07:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-08-28 14:07:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-28 14:07:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-08-28 14:07:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-08-28 14:07:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:07:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:07:20.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:07:20.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:07:20.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:07:20.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:07:20.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:07:20.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:07:20.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:07:21.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:07:22.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:07:22.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:07:23.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:07:24.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:07:25.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:07:25.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:07:26.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:07:27.265 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:07:27.266 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:07:27.266 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:07:27.266 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:07:27.273 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.92 ms, Average inference time: 7.05 ms

2025-08-28 14:07:27.274 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:07:27.355 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:07:27.477 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch328
2025-08-28 14:07:30.461 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 328/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 3.5, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 8.583e-04, size: 448, ETA: 1:31:08
2025-08-28 14:07:33.379 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 328/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 8.575e-04, size: 416, ETA: 1:31:04
2025-08-28 14:07:36.328 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 328/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 8.567e-04, size: 320, ETA: 1:31:01
2025-08-28 14:07:39.362 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 328/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 8.559e-04, size: 256, ETA: 1:30:58
2025-08-28 14:07:42.412 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 328/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.5, lr: 8.551e-04, size: 480, ETA: 1:30:55
2025-08-28 14:07:45.480 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 328/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 9.4, iou_loss: 2.9, l1_loss: 1.5, conf_loss: 3.7, cls_loss: 1.2, lr: 8.543e-04, size: 448, ETA: 1:30:51
2025-08-28 14:07:46.858 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:07:53.069 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:07:53.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:07:54.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5916
2025-08-28 14:07:54.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5310
2025-08-28 14:07:54.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3699
2025-08-28 14:07:54.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4975
2025-08-28 14:07:54.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:07:54.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:07:54.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-08-28 14:07:54.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-08-28 14:07:54.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-08-28 14:07:54.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-08-28 14:07:54.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:07:54.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:07:54.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:07:54.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:07:54.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:07:54.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:07:54.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:07:54.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:07:54.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:07:55.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:07:56.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:07:57.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:07:57.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:07:58.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:07:59.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:08:00.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:08:00.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:08:01.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:08:01.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:08:01.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 14:08:01.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:08:01.794 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.94 ms, Average inference time: 7.26 ms

2025-08-28 14:08:01.795 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:08:01.943 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:08:02.060 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch329
2025-08-28 14:08:04.976 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 329/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.9, lr: 8.531e-04, size: 448, ETA: 1:30:47
2025-08-28 14:08:08.163 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 329/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 8.9, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 4.0, cls_loss: 1.0, lr: 8.523e-04, size: 352, ETA: 1:30:44
2025-08-28 14:08:11.196 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 329/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.8, lr: 8.515e-04, size: 352, ETA: 1:30:40
2025-08-28 14:08:14.206 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 329/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.8, lr: 8.507e-04, size: 384, ETA: 1:30:37
2025-08-28 14:08:17.293 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 329/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.8, lr: 8.499e-04, size: 576, ETA: 1:30:34
2025-08-28 14:08:20.406 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 329/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 8.491e-04, size: 352, ETA: 1:30:31
2025-08-28 14:08:21.703 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:08:27.844 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:08:28.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:08:29.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5756
2025-08-28 14:08:29.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4877
2025-08-28 14:08:29.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4071
2025-08-28 14:08:29.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4901
2025-08-28 14:08:29.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:08:29.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:08:29.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-08-28 14:08:29.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-08-28 14:08:29.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-28 14:08:29.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-08-28 14:08:29.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:08:29.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:08:29.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:08:29.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:08:29.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:08:29.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:08:29.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:08:29.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:08:29.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:08:30.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:08:30.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:08:31.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:08:32.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:08:33.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:08:33.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:08:34.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:08:35.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:08:35.833 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:08:35.833 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:08:35.833 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:08:35.833 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:08:35.840 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.93 ms, Average inference time: 7.17 ms

2025-08-28 14:08:35.842 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:08:35.925 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:08:36.007 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch330
2025-08-28 14:08:38.859 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 330/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 8.479e-04, size: 288, ETA: 1:30:26
2025-08-28 14:08:41.773 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 330/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 9.5, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 4.1, cls_loss: 0.9, lr: 8.471e-04, size: 352, ETA: 1:30:23
2025-08-28 14:08:44.820 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 330/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 4.2, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 8.463e-04, size: 448, ETA: 1:30:19
2025-08-28 14:08:47.955 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 330/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 8.455e-04, size: 288, ETA: 1:30:16
2025-08-28 14:08:50.828 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 330/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 1.0, lr: 8.447e-04, size: 448, ETA: 1:30:13
2025-08-28 14:08:53.827 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 330/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 8.439e-04, size: 352, ETA: 1:30:10
2025-08-28 14:08:55.210 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:09:01.343 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:09:02.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:09:02.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5905
2025-08-28 14:09:03.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4984
2025-08-28 14:09:03.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3694
2025-08-28 14:09:03.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4861
2025-08-28 14:09:03.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:09:03.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:09:03.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.591
2025-08-28 14:09:03.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-28 14:09:03.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-28 14:09:03.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.486
2025-08-28 14:09:03.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:09:03.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:09:03.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:09:03.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:09:03.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:09:03.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:09:03.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:09:03.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:09:03.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:09:03.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:09:04.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:09:05.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:09:06.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:09:07.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:09:07.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:09:08.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:09:09.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:09:10.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:09:10.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:09:10.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:09:10.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:09:10.374 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.92 ms, Average inference time: 7.18 ms

2025-08-28 14:09:10.375 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:09:10.452 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:09:10.531 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch331
2025-08-28 14:09:13.409 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 331/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 8.428e-04, size: 512, ETA: 1:30:05
2025-08-28 14:09:16.385 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 331/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 8.420e-04, size: 256, ETA: 1:30:02
2025-08-28 14:09:19.423 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 331/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.005s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.6, lr: 8.412e-04, size: 448, ETA: 1:29:59
2025-08-28 14:09:22.518 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 331/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.6, lr: 8.404e-04, size: 544, ETA: 1:29:55
2025-08-28 14:09:25.493 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 331/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 8.396e-04, size: 448, ETA: 1:29:52
2025-08-28 14:09:28.634 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 331/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 8.388e-04, size: 416, ETA: 1:29:49
2025-08-28 14:09:30.017 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:09:36.353 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:09:37.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:09:38.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5716
2025-08-28 14:09:38.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4596
2025-08-28 14:09:38.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4083
2025-08-28 14:09:38.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4798
2025-08-28 14:09:38.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:09:38.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:09:38.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-08-28 14:09:38.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-08-28 14:09:38.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.408
2025-08-28 14:09:38.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.480
2025-08-28 14:09:38.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:09:38.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:09:38.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:09:38.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:09:38.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:09:38.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:09:38.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:09:38.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:09:38.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:09:39.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:09:40.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:09:41.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:09:42.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:09:43.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:09:44.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:09:45.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:09:46.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:09:47.679 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:09:47.679 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 14:09:47.679 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:09:47.679 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:09:47.686 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.96 ms, Average inference time: 7.19 ms

2025-08-28 14:09:47.687 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:09:47.766 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:09:47.848 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch332
2025-08-28 14:09:50.749 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 332/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 8.376e-04, size: 544, ETA: 1:29:44
2025-08-28 14:09:53.697 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 332/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 8.368e-04, size: 256, ETA: 1:29:41
2025-08-28 14:09:56.659 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 332/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 8.360e-04, size: 288, ETA: 1:29:38
2025-08-28 14:09:59.631 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 332/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 8.3, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 3.8, cls_loss: 0.7, lr: 8.352e-04, size: 448, ETA: 1:29:35
2025-08-28 14:10:02.708 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 332/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 8.344e-04, size: 288, ETA: 1:29:31
2025-08-28 14:10:05.807 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 332/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.9, lr: 8.336e-04, size: 320, ETA: 1:29:28
2025-08-28 14:10:07.162 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:10:13.152 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:10:13.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:10:14.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5821
2025-08-28 14:10:14.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4905
2025-08-28 14:10:14.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3802
2025-08-28 14:10:14.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4843
2025-08-28 14:10:14.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:10:14.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:10:14.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-08-28 14:10:14.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-08-28 14:10:14.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-08-28 14:10:14.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-08-28 14:10:14.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:10:14.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:10:14.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:10:14.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:10:14.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:10:14.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:10:14.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:10:14.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:10:14.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:10:14.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:10:15.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:10:15.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:10:16.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:10:16.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:10:17.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:10:17.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:10:18.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:10:18.982 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:10:18.982 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:10:18.982 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:10:18.983 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:10:18.989 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.91 ms, Average inference time: 7.00 ms

2025-08-28 14:10:18.990 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:10:19.067 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:10:19.146 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch333
2025-08-28 14:10:22.014 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 333/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 8.324e-04, size: 352, ETA: 1:29:23
2025-08-28 14:10:25.068 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 333/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.1, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 0.8, cls_loss: 0.5, lr: 8.316e-04, size: 416, ETA: 1:29:20
2025-08-28 14:10:28.153 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 333/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 8.308e-04, size: 576, ETA: 1:29:17
2025-08-28 14:10:31.221 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 333/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 8.300e-04, size: 256, ETA: 1:29:14
2025-08-28 14:10:34.329 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 333/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 0.8, lr: 8.292e-04, size: 576, ETA: 1:29:11
2025-08-28 14:10:37.545 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 333/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 8.284e-04, size: 256, ETA: 1:29:08
2025-08-28 14:10:38.832 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:10:45.123 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:10:45.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:10:46.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5531
2025-08-28 14:10:46.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4926
2025-08-28 14:10:46.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3816
2025-08-28 14:10:46.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4758
2025-08-28 14:10:46.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:10:46.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:10:46.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.553
2025-08-28 14:10:46.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-08-28 14:10:46.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-08-28 14:10:46.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.476
2025-08-28 14:10:46.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:10:46.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:10:46.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:10:46.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:10:46.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:10:46.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:10:46.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:10:46.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:10:46.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:10:47.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:10:48.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:10:48.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:10:49.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:10:50.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:10:50.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:10:51.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:10:52.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:10:52.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:10:52.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:10:52.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:10:52.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:10:52.944 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.95 ms, Average inference time: 7.14 ms

2025-08-28 14:10:52.945 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:10:53.023 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:10:53.105 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch334
2025-08-28 14:10:56.015 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 334/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.6, lr: 8.273e-04, size: 384, ETA: 1:29:03
2025-08-28 14:10:58.988 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 334/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 8.265e-04, size: 352, ETA: 1:29:00
2025-08-28 14:11:02.006 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 334/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.6, lr: 8.257e-04, size: 576, ETA: 1:28:56
2025-08-28 14:11:05.102 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 334/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 8.249e-04, size: 320, ETA: 1:28:53
2025-08-28 14:11:08.257 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 334/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 8.241e-04, size: 448, ETA: 1:28:50
2025-08-28 14:11:11.252 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 334/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 8.6, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 3.5, cls_loss: 0.9, lr: 8.233e-04, size: 352, ETA: 1:28:47
2025-08-28 14:11:12.652 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:11:18.751 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:11:19.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:11:19.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5831
2025-08-28 14:11:19.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5069
2025-08-28 14:11:19.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3712
2025-08-28 14:11:19.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4871
2025-08-28 14:11:19.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:11:19.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:11:19.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.583
2025-08-28 14:11:19.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-08-28 14:11:19.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-08-28 14:11:19.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-08-28 14:11:19.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:11:19.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:11:19.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:11:19.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:11:19.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:11:19.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:11:19.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:11:19.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:11:19.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:11:20.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:11:21.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:11:21.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:11:22.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:11:22.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:11:23.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:11:23.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:11:24.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:11:25.012 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:11:25.012 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:11:25.012 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:11:25.012 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:11:25.019 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.91 ms, Average inference time: 7.11 ms

2025-08-28 14:11:25.020 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:11:25.104 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:11:25.181 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch335
2025-08-28 14:11:28.379 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 335/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 8.221e-04, size: 384, ETA: 1:28:42
2025-08-28 14:11:31.367 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 335/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 8.213e-04, size: 320, ETA: 1:28:39
2025-08-28 14:11:34.347 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 335/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.9, lr: 8.205e-04, size: 320, ETA: 1:28:36
2025-08-28 14:11:37.449 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 335/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.7, conf_loss: 3.2, cls_loss: 0.7, lr: 8.197e-04, size: 416, ETA: 1:28:33
2025-08-28 14:11:40.601 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 335/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.4, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.7, lr: 8.189e-04, size: 448, ETA: 1:28:30
2025-08-28 14:11:43.574 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 335/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 8.6, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.8, cls_loss: 0.7, lr: 8.181e-04, size: 416, ETA: 1:28:27
2025-08-28 14:11:44.889 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:11:51.052 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:11:51.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:11:52.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5737
2025-08-28 14:11:52.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4654
2025-08-28 14:11:52.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4075
2025-08-28 14:11:52.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4822
2025-08-28 14:11:52.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:11:52.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:11:52.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-08-28 14:11:52.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.465
2025-08-28 14:11:52.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-28 14:11:52.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.482
2025-08-28 14:11:52.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:11:52.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:11:52.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:11:52.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:11:52.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:11:52.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:11:52.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:11:52.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:11:52.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:11:52.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:11:53.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:11:53.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:11:54.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:11:54.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:11:55.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:11:55.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:11:56.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:11:56.868 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:11:56.868 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:11:56.868 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:11:56.869 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:11:56.875 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.89 ms, Average inference time: 7.04 ms

2025-08-28 14:11:56.877 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:11:56.964 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:11:57.044 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch336
2025-08-28 14:11:59.869 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 336/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.7, lr: 8.170e-04, size: 480, ETA: 1:28:22
2025-08-28 14:12:02.911 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 336/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 8.162e-04, size: 448, ETA: 1:28:18
2025-08-28 14:12:05.977 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 336/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.8, lr: 8.154e-04, size: 320, ETA: 1:28:15
2025-08-28 14:12:08.969 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 336/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 0.8, lr: 8.146e-04, size: 480, ETA: 1:28:12
2025-08-28 14:12:12.071 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 336/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.5, lr: 8.138e-04, size: 256, ETA: 1:28:09
2025-08-28 14:12:15.112 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 336/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.5, cls_loss: 0.7, lr: 8.130e-04, size: 544, ETA: 1:28:06
2025-08-28 14:12:16.460 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:12:22.560 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:12:23.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:12:23.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5823
2025-08-28 14:12:23.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4873
2025-08-28 14:12:23.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3399
2025-08-28 14:12:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4698
2025-08-28 14:12:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:12:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:12:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-08-28 14:12:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-08-28 14:12:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-08-28 14:12:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.470
2025-08-28 14:12:23.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:12:23.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:12:23.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:12:23.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:12:23.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:12:23.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:12:23.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:12:23.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:12:23.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:12:24.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:12:25.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:12:25.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:12:26.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:12:26.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:12:27.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:12:28.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:12:28.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:12:29.237 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:12:29.237 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:12:29.237 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:12:29.237 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:12:29.244 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.92 ms, Average inference time: 7.06 ms

2025-08-28 14:12:29.245 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:12:29.330 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:12:29.408 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch337
2025-08-28 14:12:32.274 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 337/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.6, lr: 8.118e-04, size: 352, ETA: 1:28:01
2025-08-28 14:12:35.251 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 337/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.7, lr: 8.110e-04, size: 480, ETA: 1:27:58
2025-08-28 14:12:38.314 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 337/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 8.102e-04, size: 384, ETA: 1:27:55
2025-08-28 14:12:41.341 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 337/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 8.094e-04, size: 384, ETA: 1:27:51
2025-08-28 14:12:44.306 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 337/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 8.086e-04, size: 384, ETA: 1:27:48
2025-08-28 14:12:47.323 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 337/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 8.078e-04, size: 512, ETA: 1:27:45
2025-08-28 14:12:48.682 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:12:54.810 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:12:55.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:12:56.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5294
2025-08-28 14:12:56.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4960
2025-08-28 14:12:56.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3313
2025-08-28 14:12:56.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4522
2025-08-28 14:12:56.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:12:56.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:12:56.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-08-28 14:12:56.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-08-28 14:12:56.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.331
2025-08-28 14:12:56.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.452
2025-08-28 14:12:56.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:12:56.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:12:56.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:12:56.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:12:56.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:12:56.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:12:56.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:12:56.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:12:56.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:12:57.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:12:57.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:12:58.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:12:59.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:12:59.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:13:00.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:13:01.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:13:01.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:13:02.348 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:13:02.348 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 14:13:02.348 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 14:13:02.349 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:13:02.356 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.34 ms, Average NMS time: 0.93 ms, Average inference time: 7.27 ms

2025-08-28 14:13:02.357 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:13:02.439 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:13:02.520 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch338
2025-08-28 14:13:05.460 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 338/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 8.067e-04, size: 448, ETA: 1:27:40
2025-08-28 14:13:08.594 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 338/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 8.059e-04, size: 320, ETA: 1:27:37
2025-08-28 14:13:11.587 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 338/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 8.051e-04, size: 480, ETA: 1:27:34
2025-08-28 14:13:14.625 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 338/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.6, lr: 8.043e-04, size: 576, ETA: 1:27:31
2025-08-28 14:13:17.669 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 338/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 8.035e-04, size: 384, ETA: 1:27:28
2025-08-28 14:13:20.616 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 338/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.4, l1_loss: 1.4, conf_loss: 2.2, cls_loss: 1.6, lr: 8.027e-04, size: 544, ETA: 1:27:24
2025-08-28 14:13:22.035 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:13:28.391 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:13:29.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:13:29.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5708
2025-08-28 14:13:29.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5167
2025-08-28 14:13:29.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3689
2025-08-28 14:13:29.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4855
2025-08-28 14:13:29.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:13:29.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:13:29.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.571
2025-08-28 14:13:29.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-08-28 14:13:29.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-28 14:13:29.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.486
2025-08-28 14:13:29.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:13:29.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:13:29.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:13:29.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:13:29.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:13:29.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:13:29.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:13:29.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:13:29.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:13:30.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:13:30.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:13:31.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:13:32.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:13:32.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:13:33.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:13:33.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:13:34.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:13:34.925 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:13:34.926 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:13:34.926 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:13:34.926 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:13:34.933 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.94 ms, Average inference time: 7.13 ms

2025-08-28 14:13:34.934 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:13:35.011 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:13:35.091 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch339
2025-08-28 14:13:37.991 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 339/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 8.015e-04, size: 320, ETA: 1:27:20
2025-08-28 14:13:40.979 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 339/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 8.008e-04, size: 448, ETA: 1:27:16
2025-08-28 14:13:43.954 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 339/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 8.000e-04, size: 448, ETA: 1:27:13
2025-08-28 14:13:46.974 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 339/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 7.992e-04, size: 448, ETA: 1:27:10
2025-08-28 14:13:50.063 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 339/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 7.984e-04, size: 288, ETA: 1:27:07
2025-08-28 14:13:53.070 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 339/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 7.976e-04, size: 544, ETA: 1:27:04
2025-08-28 14:13:54.473 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:14:00.684 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:14:01.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:14:02.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5646
2025-08-28 14:14:02.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4865
2025-08-28 14:14:02.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3814
2025-08-28 14:14:02.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4775
2025-08-28 14:14:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:14:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:14:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.565
2025-08-28 14:14:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-08-28 14:14:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-08-28 14:14:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-08-28 14:14:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:14:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:14:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:14:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:14:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:14:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:14:02.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:14:02.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:14:02.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:14:02.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:14:03.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:14:04.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:14:05.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:14:05.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:14:06.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:14:07.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:14:07.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:14:08.624 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:14:08.624 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:14:08.624 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:14:08.625 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:14:08.637 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.94 ms, Average inference time: 7.18 ms

2025-08-28 14:14:08.638 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:14:08.718 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:14:08.800 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch340
2025-08-28 14:14:11.644 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 340/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 7.964e-04, size: 320, ETA: 1:26:59
2025-08-28 14:14:14.679 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 340/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 1.0, lr: 7.956e-04, size: 256, ETA: 1:26:56
2025-08-28 14:14:17.720 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 340/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.8, lr: 7.948e-04, size: 544, ETA: 1:26:52
2025-08-28 14:14:20.703 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 340/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 3.4, cls_loss: 0.8, lr: 7.940e-04, size: 352, ETA: 1:26:49
2025-08-28 14:14:23.769 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 340/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.9, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 3.9, cls_loss: 0.9, lr: 7.932e-04, size: 448, ETA: 1:26:46
2025-08-28 14:14:26.760 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 340/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 1.6, cls_loss: 0.8, lr: 7.924e-04, size: 480, ETA: 1:26:43
2025-08-28 14:14:28.152 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:14:34.270 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:14:34.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:14:35.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5687
2025-08-28 14:14:35.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5147
2025-08-28 14:14:35.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3902
2025-08-28 14:14:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4912
2025-08-28 14:14:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:14:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:14:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.569
2025-08-28 14:14:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 14:14:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-28 14:14:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.491
2025-08-28 14:14:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:14:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:14:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:14:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:14:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:14:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:14:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:14:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:14:35.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:14:36.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:14:36.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:14:37.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:14:37.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:14:38.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:14:38.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:14:39.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:14:40.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:14:40.578 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:14:40.579 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:14:40.579 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:14:40.579 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:14:40.586 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.94 ms, Average inference time: 7.23 ms

2025-08-28 14:14:40.587 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:14:40.745 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:14:40.816 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch341
2025-08-28 14:14:43.709 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 341/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 7.913e-04, size: 448, ETA: 1:26:38
2025-08-28 14:14:46.798 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 341/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.9, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 4.6, cls_loss: 0.6, lr: 7.905e-04, size: 480, ETA: 1:26:35
2025-08-28 14:14:49.923 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 341/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 7.897e-04, size: 384, ETA: 1:26:32
2025-08-28 14:14:53.116 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 341/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.7, lr: 7.889e-04, size: 544, ETA: 1:26:29
2025-08-28 14:14:56.150 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 341/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.6, lr: 7.881e-04, size: 320, ETA: 1:26:26
2025-08-28 14:14:59.125 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 341/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 1.0, lr: 7.873e-04, size: 320, ETA: 1:26:22
2025-08-28 14:15:00.644 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:15:06.822 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:15:07.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:15:07.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5477
2025-08-28 14:15:07.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4806
2025-08-28 14:15:07.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3781
2025-08-28 14:15:07.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4688
2025-08-28 14:15:07.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:15:07.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:15:07.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.548
2025-08-28 14:15:07.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-08-28 14:15:07.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-08-28 14:15:07.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.469
2025-08-28 14:15:07.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:15:07.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:15:07.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:15:07.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:15:07.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:15:07.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:15:07.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:15:07.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:15:07.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:15:08.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:15:08.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:15:09.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:15:09.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:15:10.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:15:10.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:15:11.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:15:11.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:15:12.254 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:15:12.254 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 14:15:12.255 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:15:12.255 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:15:12.267 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.89 ms, Average inference time: 7.08 ms

2025-08-28 14:15:12.283 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:15:12.423 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:15:12.492 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch342
2025-08-28 14:15:15.398 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 342/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.8, lr: 7.862e-04, size: 384, ETA: 1:26:18
2025-08-28 14:15:18.474 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 342/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 7.854e-04, size: 544, ETA: 1:26:15
2025-08-28 14:15:21.538 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 342/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 7.846e-04, size: 384, ETA: 1:26:11
2025-08-28 14:15:24.489 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 342/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 7.838e-04, size: 448, ETA: 1:26:08
2025-08-28 14:15:27.553 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 342/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 7.830e-04, size: 416, ETA: 1:26:05
2025-08-28 14:15:30.529 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 342/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 7.822e-04, size: 384, ETA: 1:26:02
2025-08-28 14:15:31.898 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:15:38.272 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:15:39.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:15:39.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5906
2025-08-28 14:15:40.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5039
2025-08-28 14:15:40.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3850
2025-08-28 14:15:40.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4932
2025-08-28 14:15:40.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:15:40.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:15:40.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.591
2025-08-28 14:15:40.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-08-28 14:15:40.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-28 14:15:40.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-08-28 14:15:40.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:15:40.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:15:40.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:15:40.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:15:40.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:15:40.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:15:40.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:15:40.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:15:40.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:15:40.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:15:41.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:15:42.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:15:43.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:15:44.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:15:45.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:15:45.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:15:46.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:15:47.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:15:47.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:15:47.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:15:47.591 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:15:47.598 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.93 ms, Average inference time: 7.10 ms

2025-08-28 14:15:47.599 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:15:47.679 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:15:47.758 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch343
2025-08-28 14:15:50.706 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 343/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.5, conf_loss: 2.4, cls_loss: 0.7, lr: 7.811e-04, size: 576, ETA: 1:25:57
2025-08-28 14:15:53.779 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 343/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.6, lr: 7.803e-04, size: 256, ETA: 1:25:54
2025-08-28 14:15:56.783 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 343/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 7.795e-04, size: 320, ETA: 1:25:51
2025-08-28 14:15:59.865 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 343/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.8, lr: 7.787e-04, size: 384, ETA: 1:25:48
2025-08-28 14:16:03.058 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 343/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.7, lr: 7.779e-04, size: 448, ETA: 1:25:45
2025-08-28 14:16:06.074 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 343/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.7, lr: 7.771e-04, size: 416, ETA: 1:25:41
2025-08-28 14:16:07.402 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:16:13.589 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:16:15.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:16:15.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5724
2025-08-28 14:16:16.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4880
2025-08-28 14:16:16.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3789
2025-08-28 14:16:16.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4798
2025-08-28 14:16:16.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:16:16.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:16:16.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-08-28 14:16:16.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-08-28 14:16:16.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-08-28 14:16:16.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.480
2025-08-28 14:16:16.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:16:16.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:16:16.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:16:16.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:16:16.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:16:16.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:16:16.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:16:16.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:16:16.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:16:17.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:16:18.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:16:19.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:16:21.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:16:22.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:16:23.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:16:24.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:16:25.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:16:27.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:16:27.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:16:27.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:16:27.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:16:27.101 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.95 ms, Average inference time: 7.23 ms

2025-08-28 14:16:27.102 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:16:27.185 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:16:27.268 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch344
2025-08-28 14:16:30.107 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 344/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 7.760e-04, size: 288, ETA: 1:25:37
2025-08-28 14:16:33.192 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 344/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 7.752e-04, size: 288, ETA: 1:25:33
2025-08-28 14:16:36.250 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 344/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 7.4, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 3.2, cls_loss: 0.7, lr: 7.744e-04, size: 576, ETA: 1:25:30
2025-08-28 14:16:39.440 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 344/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 7.736e-04, size: 480, ETA: 1:25:27
2025-08-28 14:16:42.633 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 344/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.8, lr: 7.728e-04, size: 544, ETA: 1:25:24
2025-08-28 14:16:45.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 344/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 0.8, cls_loss: 0.6, lr: 7.720e-04, size: 320, ETA: 1:25:21
2025-08-28 14:16:47.069 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:16:53.392 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:16:54.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:16:54.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5793
2025-08-28 14:16:54.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5033
2025-08-28 14:16:54.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3541
2025-08-28 14:16:54.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4789
2025-08-28 14:16:54.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:16:54.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:16:54.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-08-28 14:16:54.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-08-28 14:16:54.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.354
2025-08-28 14:16:54.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.479
2025-08-28 14:16:54.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:16:54.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:16:54.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:16:54.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:16:54.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:16:54.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:16:54.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:16:54.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:16:54.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:16:55.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:16:56.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:16:56.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:16:57.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:16:57.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:16:58.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:16:59.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:16:59.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:17:00.229 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:17:00.229 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 14:17:00.229 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:17:00.230 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:17:00.236 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.89 ms, Average inference time: 7.15 ms

2025-08-28 14:17:00.238 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:17:00.315 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:17:00.395 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch345
2025-08-28 14:17:03.210 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 345/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 7.709e-04, size: 320, ETA: 1:25:16
2025-08-28 14:17:06.287 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 345/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 7.701e-04, size: 544, ETA: 1:25:13
2025-08-28 14:17:09.431 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 345/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 8.6, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.3, cls_loss: 1.2, lr: 7.693e-04, size: 320, ETA: 1:25:10
2025-08-28 14:17:12.400 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 345/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.6, lr: 7.685e-04, size: 448, ETA: 1:25:07
2025-08-28 14:17:15.345 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 345/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 7.677e-04, size: 416, ETA: 1:25:03
2025-08-28 14:17:18.363 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 345/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.5, lr: 7.669e-04, size: 576, ETA: 1:25:00
2025-08-28 14:17:19.806 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:17:26.092 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:17:26.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:17:27.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5545
2025-08-28 14:17:27.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4962
2025-08-28 14:17:27.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3643
2025-08-28 14:17:27.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4717
2025-08-28 14:17:27.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:17:27.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:17:27.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.554
2025-08-28 14:17:27.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-08-28 14:17:27.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-08-28 14:17:27.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.472
2025-08-28 14:17:27.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:17:27.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:17:27.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:17:27.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:17:27.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:17:27.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:17:27.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:17:27.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:17:27.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:17:28.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:17:29.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:17:30.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:17:30.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:17:31.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:17:32.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:17:32.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:17:33.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:17:34.391 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:17:34.392 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 14:17:34.392 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:17:34.393 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:17:34.405 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.90 ms, Average inference time: 7.07 ms

2025-08-28 14:17:34.406 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:17:34.583 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:17:34.688 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch346
2025-08-28 14:17:37.703 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 346/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 7.658e-04, size: 576, ETA: 1:24:56
2025-08-28 14:17:40.893 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 346/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 7.650e-04, size: 384, ETA: 1:24:53
2025-08-28 14:17:43.920 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 346/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 1.1, lr: 7.642e-04, size: 384, ETA: 1:24:49
2025-08-28 14:17:46.820 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 346/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 7.634e-04, size: 256, ETA: 1:24:46
2025-08-28 14:17:49.810 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 346/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.0, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 0.6, lr: 7.626e-04, size: 576, ETA: 1:24:43
2025-08-28 14:17:52.865 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 346/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 7.618e-04, size: 416, ETA: 1:24:40
2025-08-28 14:17:54.215 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:18:00.375 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:18:01.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:18:02.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5871
2025-08-28 14:18:02.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5199
2025-08-28 14:18:02.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3602
2025-08-28 14:18:02.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4890
2025-08-28 14:18:02.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:18:02.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:18:02.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.587
2025-08-28 14:18:02.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-08-28 14:18:02.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.360
2025-08-28 14:18:02.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.489
2025-08-28 14:18:02.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:18:02.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:18:02.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:18:02.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:18:02.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:18:02.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:18:02.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:18:02.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:18:02.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:18:03.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:18:04.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:18:04.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:18:05.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:18:06.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:18:07.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:18:08.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:18:09.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:18:10.003 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:18:10.003 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:18:10.003 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:18:10.004 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:18:10.010 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.96 ms, Average inference time: 7.22 ms

2025-08-28 14:18:10.012 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:18:10.089 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:18:10.168 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch347
2025-08-28 14:18:13.094 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 347/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 7.607e-04, size: 512, ETA: 1:24:35
2025-08-28 14:18:16.002 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 347/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.5, lr: 7.599e-04, size: 256, ETA: 1:24:32
2025-08-28 14:18:19.158 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 347/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 7.591e-04, size: 288, ETA: 1:24:29
2025-08-28 14:18:22.024 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 347/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 7.583e-04, size: 384, ETA: 1:24:25
2025-08-28 14:18:24.961 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 347/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 1.0, lr: 7.575e-04, size: 352, ETA: 1:24:22
2025-08-28 14:18:27.905 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 347/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 7.567e-04, size: 288, ETA: 1:24:19
2025-08-28 14:18:29.167 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:18:35.341 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:18:36.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:18:37.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5470
2025-08-28 14:18:37.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4833
2025-08-28 14:18:37.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3320
2025-08-28 14:18:37.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4541
2025-08-28 14:18:37.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:18:37.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:18:37.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.547
2025-08-28 14:18:37.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.483
2025-08-28 14:18:37.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.332
2025-08-28 14:18:37.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.454
2025-08-28 14:18:37.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:18:37.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:18:37.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:18:37.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:18:37.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:18:37.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:18:37.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:18:37.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:18:37.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:18:38.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:18:39.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:18:40.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:18:40.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:18:41.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:18:42.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:18:43.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:18:44.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:18:45.279 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:18:45.280 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 14:18:45.280 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 14:18:45.280 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:18:45.289 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.94 ms, Average inference time: 7.11 ms

2025-08-28 14:18:45.290 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:18:45.376 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:18:45.504 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch348
2025-08-28 14:18:48.497 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 348/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.7, lr: 7.556e-04, size: 544, ETA: 1:24:14
2025-08-28 14:18:51.543 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 348/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 7.548e-04, size: 352, ETA: 1:24:11
2025-08-28 14:18:54.730 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 348/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 7.540e-04, size: 544, ETA: 1:24:08
2025-08-28 14:18:57.765 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 348/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 7.532e-04, size: 416, ETA: 1:24:05
2025-08-28 14:19:00.844 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 348/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 7.525e-04, size: 352, ETA: 1:24:02
2025-08-28 14:19:03.943 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 348/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.6, lr: 7.517e-04, size: 416, ETA: 1:23:58
2025-08-28 14:19:05.380 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:19:11.609 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:19:12.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:19:12.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5728
2025-08-28 14:19:12.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4965
2025-08-28 14:19:13.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4055
2025-08-28 14:19:13.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4916
2025-08-28 14:19:13.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:19:13.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:19:13.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.573
2025-08-28 14:19:13.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-08-28 14:19:13.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-08-28 14:19:13.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-08-28 14:19:13.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:19:13.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:19:13.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:19:13.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:19:13.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:19:13.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:19:13.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:19:13.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:19:13.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:19:13.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:19:14.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:19:14.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:19:15.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:19:16.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:19:16.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:19:17.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:19:18.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:19:18.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:19:18.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:19:18.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:19:18.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:19:18.717 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.95 ms, Average inference time: 7.13 ms

2025-08-28 14:19:18.723 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:19:18.800 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:19:18.879 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch349
2025-08-28 14:19:21.707 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 349/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 7.505e-04, size: 256, ETA: 1:23:54
2025-08-28 14:19:24.691 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 349/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.7, lr: 7.497e-04, size: 512, ETA: 1:23:51
2025-08-28 14:19:27.773 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 349/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 9.5, iou_loss: 3.4, l1_loss: 1.3, conf_loss: 3.7, cls_loss: 1.1, lr: 7.490e-04, size: 288, ETA: 1:23:47
2025-08-28 14:19:30.707 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 349/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.6, lr: 7.482e-04, size: 576, ETA: 1:23:44
2025-08-28 14:19:33.816 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 349/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 7.474e-04, size: 480, ETA: 1:23:41
2025-08-28 14:19:36.816 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 349/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.7, lr: 7.466e-04, size: 416, ETA: 1:23:38
2025-08-28 14:19:38.188 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:19:44.473 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:19:45.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:19:46.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5637
2025-08-28 14:19:46.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4890
2025-08-28 14:19:46.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3528
2025-08-28 14:19:46.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4685
2025-08-28 14:19:46.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:19:46.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:19:46.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.564
2025-08-28 14:19:46.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-08-28 14:19:46.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.353
2025-08-28 14:19:46.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.468
2025-08-28 14:19:46.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:19:46.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:19:46.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:19:46.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:19:46.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:19:46.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:19:46.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:19:46.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:19:46.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:19:47.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:19:48.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:19:48.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:19:49.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:19:50.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:19:51.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:19:52.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:19:52.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:19:53.713 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:19:53.714 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:19:53.714 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:19:53.714 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:19:53.722 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.94 ms, Average inference time: 7.19 ms

2025-08-28 14:19:53.723 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:19:53.890 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:19:53.963 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch350
2025-08-28 14:19:56.703 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 350/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.135s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.9, lr: 7.455e-04, size: 288, ETA: 1:23:33
2025-08-28 14:19:59.659 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 350/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 7.447e-04, size: 320, ETA: 1:23:30
2025-08-28 14:20:02.842 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 350/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 7.439e-04, size: 448, ETA: 1:23:27
2025-08-28 14:20:05.850 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 350/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.6, lr: 7.431e-04, size: 576, ETA: 1:23:23
2025-08-28 14:20:08.950 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 350/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.0, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.6, lr: 7.423e-04, size: 288, ETA: 1:23:20
2025-08-28 14:20:12.019 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 350/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 0.7, lr: 7.415e-04, size: 544, ETA: 1:23:17
2025-08-28 14:20:13.400 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:20:19.504 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:20:20.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:20:20.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5645
2025-08-28 14:20:20.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5114
2025-08-28 14:20:20.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3979
2025-08-28 14:20:20.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4913
2025-08-28 14:20:20.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:20:20.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:20:20.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.565
2025-08-28 14:20:20.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-08-28 14:20:20.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.398
2025-08-28 14:20:20.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.491
2025-08-28 14:20:20.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:20:20.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:20:20.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:20:20.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:20:20.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:20:20.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:20:20.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:20:20.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:20:20.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:20:21.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:20:21.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:20:22.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:20:22.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:20:23.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:20:24.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:20:24.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:20:25.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:20:25.635 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:20:25.635 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:20:25.636 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:20:25.636 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:20:25.643 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.94 ms, Average inference time: 7.18 ms

2025-08-28 14:20:25.644 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:20:25.727 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:20:25.806 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch351
2025-08-28 14:20:28.710 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 351/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 9.7, iou_loss: 3.4, l1_loss: 1.2, conf_loss: 4.3, cls_loss: 0.8, lr: 7.404e-04, size: 256, ETA: 1:23:12
2025-08-28 14:20:31.827 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 351/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 7.396e-04, size: 256, ETA: 1:23:09
2025-08-28 14:20:34.865 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 351/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.6, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.5, lr: 7.388e-04, size: 512, ETA: 1:23:06
2025-08-28 14:20:37.913 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 351/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 3.7, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.6, lr: 7.380e-04, size: 288, ETA: 1:23:03
2025-08-28 14:20:40.811 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 351/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 7.373e-04, size: 320, ETA: 1:23:00
2025-08-28 14:20:43.807 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 351/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 7.365e-04, size: 320, ETA: 1:22:57
2025-08-28 14:20:45.179 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:20:51.334 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:20:51.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:20:52.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5510
2025-08-28 14:20:52.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5057
2025-08-28 14:20:52.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3825
2025-08-28 14:20:52.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4798
2025-08-28 14:20:52.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:20:52.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:20:52.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-08-28 14:20:52.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-08-28 14:20:52.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-28 14:20:52.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.480
2025-08-28 14:20:52.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:20:52.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:20:52.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:20:52.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:20:52.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:20:52.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:20:52.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:20:52.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:20:52.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:20:52.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:20:53.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:20:53.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:20:54.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:20:54.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:20:55.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:20:55.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:20:56.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:20:56.719 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:20:56.720 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:20:56.720 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:20:56.720 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:20:56.727 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.90 ms, Average inference time: 7.12 ms

2025-08-28 14:20:56.727 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:20:56.836 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:20:56.920 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch352
2025-08-28 14:20:59.843 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 352/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.8, lr: 7.353e-04, size: 576, ETA: 1:22:52
2025-08-28 14:21:02.977 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 352/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 7.346e-04, size: 576, ETA: 1:22:49
2025-08-28 14:21:06.091 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 352/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 3.6, cls_loss: 0.8, lr: 7.338e-04, size: 512, ETA: 1:22:46
2025-08-28 14:21:09.136 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 352/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 7.330e-04, size: 352, ETA: 1:22:42
2025-08-28 14:21:12.133 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 352/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 7.322e-04, size: 512, ETA: 1:22:39
2025-08-28 14:21:15.243 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 352/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 7.314e-04, size: 384, ETA: 1:22:36
2025-08-28 14:21:16.605 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:21:22.681 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:21:23.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:21:23.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5566
2025-08-28 14:21:23.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4802
2025-08-28 14:21:23.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3386
2025-08-28 14:21:23.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4585
2025-08-28 14:21:23.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:21:23.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:21:23.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.557
2025-08-28 14:21:23.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.480
2025-08-28 14:21:23.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.339
2025-08-28 14:21:23.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.458
2025-08-28 14:21:23.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:21:23.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:21:23.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:21:23.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:21:23.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:21:23.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:21:23.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:21:23.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:21:23.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:21:24.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:21:24.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:21:24.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:21:25.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:21:25.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:21:26.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:21:26.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:21:26.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:21:27.390 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:21:27.391 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:21:27.391 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 14:21:27.391 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:21:27.397 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.85 ms, Average inference time: 7.08 ms

2025-08-28 14:21:27.398 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:21:27.480 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:21:27.557 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch353
2025-08-28 14:21:30.583 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 353/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.9, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.7, lr: 7.303e-04, size: 256, ETA: 1:22:32
2025-08-28 14:21:33.620 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 353/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.9, lr: 7.295e-04, size: 448, ETA: 1:22:28
2025-08-28 14:21:36.809 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 353/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 7.287e-04, size: 512, ETA: 1:22:25
2025-08-28 14:21:39.814 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 353/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.5, lr: 7.280e-04, size: 544, ETA: 1:22:22
2025-08-28 14:21:42.796 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 353/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.8, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.9, cls_loss: 0.9, lr: 7.272e-04, size: 480, ETA: 1:22:19
2025-08-28 14:21:45.877 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 353/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 7.264e-04, size: 576, ETA: 1:22:16
2025-08-28 14:21:47.264 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:21:53.458 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:21:54.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:21:54.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5646
2025-08-28 14:21:54.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5153
2025-08-28 14:21:54.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3090
2025-08-28 14:21:54.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4630
2025-08-28 14:21:54.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:21:54.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:21:54.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.565
2025-08-28 14:21:54.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 14:21:54.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.309
2025-08-28 14:21:54.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.463
2025-08-28 14:21:54.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:21:54.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:21:54.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:21:54.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:21:54.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:21:54.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:21:54.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:21:54.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:21:54.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:21:55.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:21:56.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:21:56.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:21:57.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:21:58.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:21:58.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:21:59.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:22:00.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:22:00.795 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:22:00.796 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 14:22:00.796 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 14:22:00.796 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:22:00.803 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.93 ms, Average inference time: 7.17 ms

2025-08-28 14:22:00.804 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:22:00.888 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:22:00.970 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch354
2025-08-28 14:22:03.844 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 354/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 7.253e-04, size: 256, ETA: 1:22:11
2025-08-28 14:22:06.814 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 354/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.7, lr: 7.245e-04, size: 320, ETA: 1:22:08
2025-08-28 14:22:09.778 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 354/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 7.237e-04, size: 352, ETA: 1:22:05
2025-08-28 14:22:12.774 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 354/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.9, cls_loss: 0.6, lr: 7.229e-04, size: 352, ETA: 1:22:01
2025-08-28 14:22:15.821 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 354/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 7.221e-04, size: 384, ETA: 1:21:58
2025-08-28 14:22:18.853 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 354/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 7.214e-04, size: 448, ETA: 1:21:55
2025-08-28 14:22:20.244 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:22:26.460 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:22:27.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:22:28.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5922
2025-08-28 14:22:28.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5093
2025-08-28 14:22:28.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3766
2025-08-28 14:22:28.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4927
2025-08-28 14:22:28.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:22:28.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:22:28.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-08-28 14:22:28.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-08-28 14:22:28.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-08-28 14:22:28.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-08-28 14:22:28.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:22:28.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:22:28.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:22:28.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:22:28.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:22:28.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:22:28.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:22:28.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:22:28.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:22:29.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:22:30.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:22:31.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:22:32.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:22:32.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:22:33.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:22:34.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:22:35.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:22:36.363 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:22:36.364 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:22:36.364 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:22:36.364 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:22:36.371 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.97 ms, Average inference time: 7.13 ms

2025-08-28 14:22:36.380 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:22:36.500 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:22:36.614 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch355
2025-08-28 14:22:39.484 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 355/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 7.202e-04, size: 288, ETA: 1:21:50
2025-08-28 14:22:42.506 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 355/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 7.195e-04, size: 352, ETA: 1:21:47
2025-08-28 14:22:45.603 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 355/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 1.1, lr: 7.187e-04, size: 448, ETA: 1:21:44
2025-08-28 14:22:48.624 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 355/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 7.179e-04, size: 544, ETA: 1:21:41
2025-08-28 14:22:51.593 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 355/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.6, lr: 7.171e-04, size: 416, ETA: 1:21:38
2025-08-28 14:22:54.542 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 355/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.8, lr: 7.163e-04, size: 256, ETA: 1:21:34
2025-08-28 14:22:55.875 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:23:02.118 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:23:02.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:23:03.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5590
2025-08-28 14:23:03.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4997
2025-08-28 14:23:03.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3831
2025-08-28 14:23:03.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4806
2025-08-28 14:23:03.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:23:03.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:23:03.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-08-28 14:23:03.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-08-28 14:23:03.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-28 14:23:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-08-28 14:23:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:23:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:23:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:23:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:23:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:23:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:23:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:23:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:23:03.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:23:04.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:23:04.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:23:05.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:23:06.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:23:06.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:23:07.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:23:08.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:23:08.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:23:09.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:23:09.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:23:09.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:23:09.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:23:09.513 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.92 ms, Average inference time: 7.19 ms

2025-08-28 14:23:09.513 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:23:09.648 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:23:09.719 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch356
2025-08-28 14:23:12.573 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 356/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 7.152e-04, size: 320, ETA: 1:21:30
2025-08-28 14:23:15.545 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 356/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 7.144e-04, size: 544, ETA: 1:21:26
2025-08-28 14:23:18.756 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 356/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 8.3, iou_loss: 2.8, l1_loss: 1.4, conf_loss: 3.3, cls_loss: 0.8, lr: 7.137e-04, size: 576, ETA: 1:21:23
2025-08-28 14:23:21.828 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 356/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 1.2, lr: 7.129e-04, size: 512, ETA: 1:21:20
2025-08-28 14:23:24.868 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 356/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.5, lr: 7.121e-04, size: 384, ETA: 1:21:17
2025-08-28 14:23:27.929 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 356/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 7.113e-04, size: 256, ETA: 1:21:14
2025-08-28 14:23:29.198 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:23:35.287 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:23:35.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:23:36.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5616
2025-08-28 14:23:36.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5005
2025-08-28 14:23:36.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3641
2025-08-28 14:23:36.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4754
2025-08-28 14:23:36.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:23:36.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:23:36.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.562
2025-08-28 14:23:36.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-08-28 14:23:36.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-08-28 14:23:36.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.475
2025-08-28 14:23:36.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:23:36.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:23:36.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:23:36.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:23:36.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:23:36.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:23:36.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:23:36.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:23:36.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:23:37.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:23:37.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:23:38.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:23:38.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:23:39.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:23:39.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:23:40.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:23:40.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:23:41.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:23:41.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:23:41.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:23:41.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:23:41.413 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.88 ms, Average inference time: 7.12 ms

2025-08-28 14:23:41.414 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:23:41.496 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:23:41.615 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch357
2025-08-28 14:23:44.597 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 357/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 7.102e-04, size: 288, ETA: 1:21:09
2025-08-28 14:23:47.624 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 357/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.8, lr: 7.094e-04, size: 480, ETA: 1:21:06
2025-08-28 14:23:50.751 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 357/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 7.086e-04, size: 320, ETA: 1:21:03
2025-08-28 14:23:53.638 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 357/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.5, lr: 7.079e-04, size: 320, ETA: 1:21:00
2025-08-28 14:23:56.653 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 357/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.6, lr: 7.071e-04, size: 544, ETA: 1:20:57
2025-08-28 14:23:59.658 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 357/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.3, l1_loss: 0.4, conf_loss: 1.6, cls_loss: 0.5, lr: 7.063e-04, size: 352, ETA: 1:20:53
2025-08-28 14:24:01.028 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:24:06.896 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:24:07.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:24:07.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.2982
2025-08-28 14:24:07.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3542
2025-08-28 14:24:07.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1829
2025-08-28 14:24:07.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.2784
2025-08-28 14:24:07.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:24:07.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:24:07.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.298
2025-08-28 14:24:07.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.354
2025-08-28 14:24:07.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.183
2025-08-28 14:24:07.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.278
2025-08-28 14:24:07.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:24:07.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:24:07.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:24:07.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:24:07.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:24:07.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:24:07.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:24:07.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:24:07.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:24:07.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:24:07.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:24:07.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:24:08.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:24:08.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:24:08.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:24:08.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:24:08.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:24:08.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:24:08.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.14
2025-08-28 14:24:08.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.28
2025-08-28 14:24:08.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:24:08.842 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.34 ms, Average NMS time: 0.65 ms, Average inference time: 6.99 ms

2025-08-28 14:24:08.843 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:24:08.930 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:24:09.006 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch358
2025-08-28 14:24:11.956 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 358/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 1.7, cls_loss: 0.5, lr: 7.052e-04, size: 256, ETA: 1:20:49
2025-08-28 14:24:14.938 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 358/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.8, lr: 7.044e-04, size: 256, ETA: 1:20:45
2025-08-28 14:24:18.075 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 358/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 0.7, lr: 7.036e-04, size: 448, ETA: 1:20:42
2025-08-28 14:24:21.127 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 358/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 7.029e-04, size: 480, ETA: 1:20:39
2025-08-28 14:24:24.190 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 358/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 11.0, iou_loss: 3.7, l1_loss: 1.4, conf_loss: 5.1, cls_loss: 0.8, lr: 7.021e-04, size: 256, ETA: 1:20:36
2025-08-28 14:24:27.150 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 358/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 3.1, cls_loss: 0.7, lr: 7.013e-04, size: 416, ETA: 1:20:33
2025-08-28 14:24:28.469 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:24:34.597 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:24:35.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:24:35.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5975
2025-08-28 14:24:36.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5290
2025-08-28 14:24:36.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3988
2025-08-28 14:24:36.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5084
2025-08-28 14:24:36.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:24:36.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:24:36.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.597
2025-08-28 14:24:36.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-08-28 14:24:36.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-08-28 14:24:36.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.508
2025-08-28 14:24:36.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:24:36.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:24:36.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:24:36.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:24:36.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:24:36.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:24:36.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:24:36.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:24:36.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:24:36.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:24:37.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:24:38.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:24:38.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:24:39.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:24:40.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:24:40.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:24:41.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:24:42.119 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:24:42.119 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 14:24:42.119 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 14:24:42.119 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:24:42.132 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.91 ms, Average inference time: 7.09 ms

2025-08-28 14:24:42.133 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:24:42.247 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:24:42.356 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch359
2025-08-28 14:24:45.322 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 359/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 7.002e-04, size: 416, ETA: 1:20:28
2025-08-28 14:24:48.362 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 359/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 9.3, iou_loss: 3.0, l1_loss: 1.4, conf_loss: 3.8, cls_loss: 1.1, lr: 6.994e-04, size: 576, ETA: 1:20:25
2025-08-28 14:24:51.526 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 359/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 6.986e-04, size: 512, ETA: 1:20:22
2025-08-28 14:24:54.541 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 359/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 6.979e-04, size: 512, ETA: 1:20:19
2025-08-28 14:24:57.718 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 359/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 6.971e-04, size: 352, ETA: 1:20:16
2025-08-28 14:25:00.633 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 359/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.7, lr: 6.963e-04, size: 544, ETA: 1:20:12
2025-08-28 14:25:02.015 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:25:08.194 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:25:09.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:25:09.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5765
2025-08-28 14:25:09.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5204
2025-08-28 14:25:09.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3765
2025-08-28 14:25:09.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4912
2025-08-28 14:25:09.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:25:09.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:25:09.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-08-28 14:25:09.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-08-28 14:25:09.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-08-28 14:25:09.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.491
2025-08-28 14:25:09.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:25:09.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:25:09.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:25:09.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:25:09.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:25:09.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:25:09.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:25:09.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:25:09.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:25:10.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:25:11.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:25:12.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:25:12.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:25:13.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:25:14.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:25:14.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:25:15.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:25:16.384 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:25:16.384 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:25:16.384 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:25:16.384 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:25:16.391 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.93 ms, Average inference time: 7.21 ms

2025-08-28 14:25:16.392 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:25:16.479 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:25:16.560 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch360
2025-08-28 14:25:19.627 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 360/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 3.6, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.5, lr: 6.952e-04, size: 320, ETA: 1:20:08
2025-08-28 14:25:22.640 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 360/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 6.944e-04, size: 480, ETA: 1:20:05
2025-08-28 14:25:25.602 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 360/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.7, lr: 6.936e-04, size: 384, ETA: 1:20:01
2025-08-28 14:25:28.607 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 360/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 6.929e-04, size: 448, ETA: 1:19:58
2025-08-28 14:25:31.620 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 360/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.7, lr: 6.921e-04, size: 352, ETA: 1:19:55
2025-08-28 14:25:34.604 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 360/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 6.913e-04, size: 384, ETA: 1:19:52
2025-08-28 14:25:35.969 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:25:42.252 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:25:42.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:25:43.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5921
2025-08-28 14:25:43.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5060
2025-08-28 14:25:43.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3904
2025-08-28 14:25:43.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4962
2025-08-28 14:25:43.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:25:43.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:25:43.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-08-28 14:25:43.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-08-28 14:25:43.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-28 14:25:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.496
2025-08-28 14:25:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:25:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:25:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:25:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:25:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:25:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:25:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:25:43.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:25:43.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:25:44.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:25:44.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:25:45.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:25:46.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:25:46.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:25:47.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:25:48.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:25:48.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:25:49.376 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:25:49.376 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:25:49.376 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 14:25:49.376 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:25:49.383 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.92 ms, Average inference time: 7.15 ms

2025-08-28 14:25:49.384 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:25:49.481 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:25:49.567 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch361
2025-08-28 14:25:52.422 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 361/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.7, lr: 6.902e-04, size: 448, ETA: 1:19:47
2025-08-28 14:25:55.655 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 361/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 2.3, cls_loss: 0.7, lr: 6.894e-04, size: 512, ETA: 1:19:44
2025-08-28 14:25:58.606 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 361/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.8, lr: 6.887e-04, size: 352, ETA: 1:19:41
2025-08-28 14:26:01.640 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 361/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 6.879e-04, size: 544, ETA: 1:19:38
2025-08-28 14:26:04.630 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 361/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 6.871e-04, size: 480, ETA: 1:19:35
2025-08-28 14:26:07.854 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 361/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 9.8, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 5.2, cls_loss: 0.7, lr: 6.864e-04, size: 576, ETA: 1:19:32
2025-08-28 14:26:09.284 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:26:15.394 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:26:16.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:26:16.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5370
2025-08-28 14:26:16.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4706
2025-08-28 14:26:16.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3848
2025-08-28 14:26:16.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4641
2025-08-28 14:26:16.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:26:16.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:26:16.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-08-28 14:26:16.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-08-28 14:26:16.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-28 14:26:16.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.464
2025-08-28 14:26:16.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:26:16.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:26:16.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:26:16.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:26:16.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:26:16.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:26:16.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:26:16.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:26:16.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:26:17.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:26:17.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:26:18.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:26:19.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:26:19.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:26:20.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:26:20.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:26:21.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:26:22.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:26:22.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 14:26:22.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 14:26:22.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:26:22.091 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.35 ms, Average NMS time: 0.93 ms, Average inference time: 7.28 ms

2025-08-28 14:26:22.092 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:26:22.177 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:26:22.257 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch362
2025-08-28 14:26:25.088 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 362/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 6.852e-04, size: 352, ETA: 1:19:27
2025-08-28 14:26:28.183 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 362/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.005s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.5, lr: 6.845e-04, size: 320, ETA: 1:19:24
2025-08-28 14:26:31.144 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 362/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.7, lr: 6.837e-04, size: 352, ETA: 1:19:21
2025-08-28 14:26:34.099 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 362/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 6.829e-04, size: 416, ETA: 1:19:17
2025-08-28 14:26:37.034 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 362/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 6.822e-04, size: 320, ETA: 1:19:14
2025-08-28 14:26:39.991 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 362/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 9.3, iou_loss: 3.4, l1_loss: 1.5, conf_loss: 3.6, cls_loss: 0.8, lr: 6.814e-04, size: 416, ETA: 1:19:11
2025-08-28 14:26:41.443 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:26:47.561 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:26:48.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:26:48.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5675
2025-08-28 14:26:48.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5068
2025-08-28 14:26:48.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3877
2025-08-28 14:26:48.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4873
2025-08-28 14:26:48.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:26:48.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:26:48.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.568
2025-08-28 14:26:48.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-08-28 14:26:48.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-08-28 14:26:48.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-08-28 14:26:48.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:26:48.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:26:48.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:26:48.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:26:48.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:26:48.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:26:48.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:26:48.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:26:48.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:26:49.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:26:50.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:26:50.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:26:51.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:26:52.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:26:52.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:26:53.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:26:53.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:26:54.540 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:26:54.540 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:26:54.540 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:26:54.540 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:26:54.547 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.90 ms, Average inference time: 7.18 ms

2025-08-28 14:26:54.548 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:26:54.666 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:26:54.738 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch363
2025-08-28 14:26:57.716 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 363/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.5, lr: 6.803e-04, size: 544, ETA: 1:19:06
2025-08-28 14:27:00.711 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 363/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.4, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.4, lr: 6.795e-04, size: 480, ETA: 1:19:03
2025-08-28 14:27:03.691 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 363/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 6.787e-04, size: 416, ETA: 1:19:00
2025-08-28 14:27:06.615 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 363/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.7, lr: 6.780e-04, size: 288, ETA: 1:18:57
2025-08-28 14:27:09.708 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 363/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.9, lr: 6.772e-04, size: 416, ETA: 1:18:54
2025-08-28 14:27:12.777 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 363/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 3.6, cls_loss: 1.0, lr: 6.764e-04, size: 352, ETA: 1:18:50
2025-08-28 14:27:14.066 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:27:20.324 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:27:21.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:27:21.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5847
2025-08-28 14:27:21.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5032
2025-08-28 14:27:21.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3631
2025-08-28 14:27:21.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4837
2025-08-28 14:27:21.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:27:21.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:27:21.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.585
2025-08-28 14:27:21.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-08-28 14:27:21.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-08-28 14:27:21.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-08-28 14:27:21.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:27:21.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:27:21.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:27:21.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:27:21.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:27:21.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:27:21.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:27:21.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:27:21.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:27:22.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:27:22.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:27:23.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:27:24.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:27:24.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:27:25.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:27:25.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:27:26.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:27:27.241 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:27:27.241 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 14:27:27.241 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:27:27.241 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:27:27.248 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.93 ms, Average inference time: 7.06 ms

2025-08-28 14:27:27.249 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:27:27.365 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:27:27.439 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch364
2025-08-28 14:27:30.274 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 364/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 4.0, cls_loss: 0.6, lr: 6.753e-04, size: 512, ETA: 1:18:46
2025-08-28 14:27:33.419 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 364/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 3.2, cls_loss: 0.8, lr: 6.745e-04, size: 384, ETA: 1:18:43
2025-08-28 14:27:36.429 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 364/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.8, lr: 6.738e-04, size: 320, ETA: 1:18:39
2025-08-28 14:27:39.381 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 364/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 6.730e-04, size: 576, ETA: 1:18:36
2025-08-28 14:27:42.465 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 364/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 6.722e-04, size: 384, ETA: 1:18:33
2025-08-28 14:27:45.428 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 364/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 6.715e-04, size: 384, ETA: 1:18:30
2025-08-28 14:27:46.757 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:27:52.936 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:27:53.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:27:53.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5220
2025-08-28 14:27:53.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4461
2025-08-28 14:27:53.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3188
2025-08-28 14:27:53.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4290
2025-08-28 14:27:53.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:27:53.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:27:53.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-08-28 14:27:53.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.446
2025-08-28 14:27:53.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.319
2025-08-28 14:27:53.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.429
2025-08-28 14:27:53.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:27:53.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:27:53.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:27:53.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:27:53.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:27:53.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:27:53.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:27:53.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:27:53.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:27:53.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:27:54.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:27:54.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:27:54.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:27:55.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:27:55.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:27:55.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:27:55.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:27:56.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:27:56.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 14:27:56.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-08-28 14:27:56.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:27:56.226 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.81 ms, Average inference time: 7.11 ms

2025-08-28 14:27:56.227 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:27:56.310 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:27:56.386 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch365
2025-08-28 14:27:59.267 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 365/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 6.704e-04, size: 576, ETA: 1:18:25
2025-08-28 14:28:02.448 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 365/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 6.696e-04, size: 576, ETA: 1:18:22
2025-08-28 14:28:05.539 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 365/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 6.688e-04, size: 544, ETA: 1:18:19
2025-08-28 14:28:08.609 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 365/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.5, lr: 6.681e-04, size: 512, ETA: 1:18:16
2025-08-28 14:28:11.697 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 365/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 6.673e-04, size: 384, ETA: 1:18:13
2025-08-28 14:28:14.690 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 365/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 6.665e-04, size: 384, ETA: 1:18:09
2025-08-28 14:28:16.025 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:28:22.378 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:28:23.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:28:23.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5702
2025-08-28 14:28:23.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4817
2025-08-28 14:28:23.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3555
2025-08-28 14:28:23.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4691
2025-08-28 14:28:23.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:28:23.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:28:23.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.570
2025-08-28 14:28:23.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-28 14:28:23.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-08-28 14:28:23.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.469
2025-08-28 14:28:23.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:28:23.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:28:23.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:28:23.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:28:23.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:28:23.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:28:23.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:28:23.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:28:23.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:28:24.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:28:24.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:28:25.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:28:25.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:28:26.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:28:26.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:28:27.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:28:27.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:28:28.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:28:28.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:28:28.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:28:28.480 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:28:28.486 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.91 ms, Average inference time: 7.17 ms

2025-08-28 14:28:28.488 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:28:28.570 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:28:28.648 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch366
2025-08-28 14:28:31.480 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 366/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.6, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.5, lr: 6.654e-04, size: 480, ETA: 1:18:05
2025-08-28 14:28:34.455 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 366/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 6.647e-04, size: 448, ETA: 1:18:02
2025-08-28 14:28:37.558 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 366/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.3, cls_loss: 0.6, lr: 6.639e-04, size: 512, ETA: 1:17:58
2025-08-28 14:28:40.502 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 366/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 6.631e-04, size: 384, ETA: 1:17:55
2025-08-28 14:28:43.537 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 366/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.0, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.6, lr: 6.624e-04, size: 544, ETA: 1:17:52
2025-08-28 14:28:46.591 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 366/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 6.616e-04, size: 320, ETA: 1:17:49
2025-08-28 14:28:47.984 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:28:54.031 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:28:54.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:28:55.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5774
2025-08-28 14:28:55.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5159
2025-08-28 14:28:55.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3849
2025-08-28 14:28:55.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4927
2025-08-28 14:28:55.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:28:55.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:28:55.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.577
2025-08-28 14:28:55.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-08-28 14:28:55.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-28 14:28:55.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-08-28 14:28:55.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:28:55.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:28:55.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:28:55.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:28:55.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:28:55.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:28:55.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:28:55.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:28:55.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:28:56.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:28:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:28:58.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:28:59.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:28:59.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:29:00.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:29:01.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:29:02.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:29:02.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:29:02.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:29:02.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:29:02.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:29:02.961 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.92 ms, Average inference time: 7.01 ms

2025-08-28 14:29:02.962 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:29:03.089 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:29:03.161 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch367
2025-08-28 14:29:06.072 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 367/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.1, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 6.605e-04, size: 288, ETA: 1:17:44
2025-08-28 14:29:09.087 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 367/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 6.597e-04, size: 416, ETA: 1:17:41
2025-08-28 14:29:12.025 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 367/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 6.590e-04, size: 480, ETA: 1:17:38
2025-08-28 14:29:15.132 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 367/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 9.6, iou_loss: 3.7, l1_loss: 1.4, conf_loss: 3.6, cls_loss: 0.8, lr: 6.582e-04, size: 256, ETA: 1:17:35
2025-08-28 14:29:18.003 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 367/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.7, lr: 6.574e-04, size: 512, ETA: 1:17:32
2025-08-28 14:29:21.111 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 367/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.6, lr: 6.567e-04, size: 512, ETA: 1:17:28
2025-08-28 14:29:22.496 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:29:28.745 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:29:29.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:29:30.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5762
2025-08-28 14:29:30.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4950
2025-08-28 14:29:30.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3784
2025-08-28 14:29:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4832
2025-08-28 14:29:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:29:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:29:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-08-28 14:29:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-08-28 14:29:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-08-28 14:29:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.483
2025-08-28 14:29:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:29:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:29:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:29:30.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:29:30.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:29:30.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:29:30.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:29:30.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:29:30.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:29:30.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:29:31.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:29:32.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:29:32.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:29:33.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:29:33.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:29:34.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:29:35.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:29:35.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:29:35.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:29:35.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:29:35.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:29:35.857 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.95 ms, Average inference time: 7.05 ms

2025-08-28 14:29:35.858 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:29:35.945 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:29:36.024 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch368
2025-08-28 14:29:38.983 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 368/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.6, lr: 6.556e-04, size: 288, ETA: 1:17:24
2025-08-28 14:29:42.072 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 368/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.005s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 6.548e-04, size: 480, ETA: 1:17:21
2025-08-28 14:29:45.228 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 368/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 4.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 0.8, cls_loss: 0.6, lr: 6.541e-04, size: 448, ETA: 1:17:18
2025-08-28 14:29:48.193 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 368/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 1.0, lr: 6.533e-04, size: 544, ETA: 1:17:14
2025-08-28 14:29:51.307 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 368/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 6.525e-04, size: 352, ETA: 1:17:11
2025-08-28 14:29:54.404 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 368/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 1.5, conf_loss: 2.1, cls_loss: 0.6, lr: 6.518e-04, size: 576, ETA: 1:17:08
2025-08-28 14:29:55.824 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:30:01.897 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:30:02.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:30:03.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5861
2025-08-28 14:30:03.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5218
2025-08-28 14:30:03.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3744
2025-08-28 14:30:03.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4941
2025-08-28 14:30:03.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:30:03.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:30:03.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-08-28 14:30:03.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-08-28 14:30:03.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.374
2025-08-28 14:30:03.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-08-28 14:30:03.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:30:03.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:30:03.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:30:03.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:30:03.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:30:03.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:30:03.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:30:03.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:30:03.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:30:04.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:30:04.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:30:05.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:30:06.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:30:06.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:30:07.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:30:08.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:30:08.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:30:09.516 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:30:09.516 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:30:09.516 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:30:09.516 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:30:09.523 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.93 ms, Average inference time: 7.14 ms

2025-08-28 14:30:09.524 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:30:09.600 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:30:09.682 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch369
2025-08-28 14:30:12.637 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 369/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 6.507e-04, size: 576, ETA: 1:17:04
2025-08-28 14:30:15.925 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 369/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.3, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 7.3, cls_loss: 0.0, lr: 6.499e-04, size: 320, ETA: 1:17:01
2025-08-28 14:30:18.883 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 369/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 6.491e-04, size: 320, ETA: 1:16:57
2025-08-28 14:30:21.827 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 369/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 6.484e-04, size: 480, ETA: 1:16:54
2025-08-28 14:30:24.891 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 369/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 6.476e-04, size: 256, ETA: 1:16:51
2025-08-28 14:30:27.878 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 369/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.8, lr: 6.469e-04, size: 512, ETA: 1:16:48
2025-08-28 14:30:29.309 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:30:35.462 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:30:36.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:30:36.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5677
2025-08-28 14:30:36.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4926
2025-08-28 14:30:36.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3923
2025-08-28 14:30:36.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4842
2025-08-28 14:30:36.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:30:36.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:30:36.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.568
2025-08-28 14:30:36.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-08-28 14:30:36.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-28 14:30:36.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-08-28 14:30:36.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:30:36.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:30:36.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:30:36.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:30:36.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:30:36.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:30:36.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:30:36.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:30:36.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:30:37.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:30:38.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:30:38.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:30:39.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:30:39.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:30:40.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:30:40.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:30:41.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:30:42.141 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:30:42.141 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:30:42.141 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:30:42.141 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:30:42.148 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.90 ms, Average inference time: 7.07 ms

2025-08-28 14:30:42.149 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:30:42.235 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:30:42.314 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch370
2025-08-28 14:30:45.198 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 370/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 6.458e-04, size: 352, ETA: 1:16:43
2025-08-28 14:30:48.307 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 370/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 6.450e-04, size: 448, ETA: 1:16:40
2025-08-28 14:30:51.260 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 370/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.8, lr: 6.442e-04, size: 512, ETA: 1:16:37
2025-08-28 14:30:54.496 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 370/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.7, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 6.435e-04, size: 256, ETA: 1:16:34
2025-08-28 14:30:57.447 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 370/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.2, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 6.427e-04, size: 352, ETA: 1:16:31
2025-08-28 14:31:00.381 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 370/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.5, lr: 6.420e-04, size: 512, ETA: 1:16:27
2025-08-28 14:31:01.737 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:31:07.887 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:31:08.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:31:08.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5667
2025-08-28 14:31:09.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5257
2025-08-28 14:31:09.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3414
2025-08-28 14:31:09.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4779
2025-08-28 14:31:09.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:31:09.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:31:09.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.567
2025-08-28 14:31:09.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-08-28 14:31:09.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.341
2025-08-28 14:31:09.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-08-28 14:31:09.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:31:09.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:31:09.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:31:09.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:31:09.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:31:09.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:31:09.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:31:09.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:31:09.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:31:09.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:31:10.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:31:10.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:31:11.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:31:11.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:31:12.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:31:12.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:31:13.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:31:14.032 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:31:14.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:31:14.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:31:14.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:31:14.040 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.89 ms, Average inference time: 7.08 ms

2025-08-28 14:31:14.041 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:31:14.118 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:31:14.197 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch371
2025-08-28 14:31:16.946 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 371/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.137s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 1.0, lr: 6.409e-04, size: 288, ETA: 1:16:23
2025-08-28 14:31:20.102 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 371/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 6.401e-04, size: 288, ETA: 1:16:20
2025-08-28 14:31:23.209 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 371/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 6.394e-04, size: 256, ETA: 1:16:16
2025-08-28 14:31:26.172 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 371/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 6.386e-04, size: 480, ETA: 1:16:13
2025-08-28 14:31:29.287 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 371/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 6.378e-04, size: 576, ETA: 1:16:10
2025-08-28 14:31:32.523 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 371/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 8.6, iou_loss: 3.5, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.9, lr: 6.371e-04, size: 352, ETA: 1:16:07
2025-08-28 14:31:33.828 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:31:39.963 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:31:40.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:31:40.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5460
2025-08-28 14:31:40.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4731
2025-08-28 14:31:40.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3453
2025-08-28 14:31:40.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4548
2025-08-28 14:31:40.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:31:40.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:31:40.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.546
2025-08-28 14:31:40.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.473
2025-08-28 14:31:40.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.345
2025-08-28 14:31:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.455
2025-08-28 14:31:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:31:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:31:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:31:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:31:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:31:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:31:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:31:40.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:31:40.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:31:41.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:31:41.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:31:41.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:31:42.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:31:42.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:31:42.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:31:43.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:31:43.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:31:43.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:31:43.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 14:31:43.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 14:31:43.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:31:43.811 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.85 ms, Average inference time: 7.09 ms

2025-08-28 14:31:43.812 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:31:43.893 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:31:43.973 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch372
2025-08-28 14:31:46.788 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 372/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.6, lr: 6.360e-04, size: 416, ETA: 1:16:02
2025-08-28 14:31:49.872 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 372/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 6.352e-04, size: 288, ETA: 1:15:59
2025-08-28 14:31:52.981 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 372/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.7, lr: 6.345e-04, size: 512, ETA: 1:15:56
2025-08-28 14:31:56.042 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 372/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 1.4, conf_loss: 1.9, cls_loss: 0.7, lr: 6.337e-04, size: 544, ETA: 1:15:53
2025-08-28 14:31:59.138 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 372/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 2.8, cls_loss: 0.6, lr: 6.330e-04, size: 544, ETA: 1:15:50
2025-08-28 14:32:02.350 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 372/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 6.322e-04, size: 512, ETA: 1:15:47
2025-08-28 14:32:03.640 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:32:09.941 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:32:11.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:32:11.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5747
2025-08-28 14:32:11.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4522
2025-08-28 14:32:11.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3883
2025-08-28 14:32:11.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4718
2025-08-28 14:32:11.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:32:11.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:32:11.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.575
2025-08-28 14:32:11.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-08-28 14:32:11.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-08-28 14:32:11.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.472
2025-08-28 14:32:11.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:32:11.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:32:11.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:32:11.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:32:11.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:32:11.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:32:11.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:32:11.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:32:11.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:32:12.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:32:13.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:32:14.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:32:15.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:32:16.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:32:17.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:32:18.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:32:19.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:32:20.169 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:32:20.169 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 14:32:20.169 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:32:20.169 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:32:20.177 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.99 ms, Average inference time: 7.27 ms

2025-08-28 14:32:20.178 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:32:20.256 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:32:20.337 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch373
2025-08-28 14:32:23.195 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 373/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.6, lr: 6.311e-04, size: 480, ETA: 1:15:42
2025-08-28 14:32:26.264 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 373/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.8, lr: 6.304e-04, size: 544, ETA: 1:15:39
2025-08-28 14:32:29.302 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 373/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.1, l1_loss: 0.4, conf_loss: 2.5, cls_loss: 0.4, lr: 6.296e-04, size: 448, ETA: 1:15:36
2025-08-28 14:32:32.275 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 373/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.7, lr: 6.289e-04, size: 416, ETA: 1:15:33
2025-08-28 14:32:35.271 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 373/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.9, cls_loss: 0.6, lr: 6.281e-04, size: 320, ETA: 1:15:29
2025-08-28 14:32:38.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 373/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.6, lr: 6.274e-04, size: 384, ETA: 1:15:26
2025-08-28 14:32:39.644 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:32:45.789 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:32:46.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:32:46.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5778
2025-08-28 14:32:46.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5043
2025-08-28 14:32:46.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3780
2025-08-28 14:32:46.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4867
2025-08-28 14:32:46.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:32:46.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:32:46.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-08-28 14:32:46.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-08-28 14:32:46.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-08-28 14:32:46.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-08-28 14:32:46.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:32:46.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:32:46.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:32:46.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:32:46.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:32:46.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:32:46.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:32:46.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:32:46.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:32:47.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:32:48.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:32:48.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:32:49.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:32:50.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:32:50.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:32:51.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:32:51.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:32:52.242 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:32:52.242 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:32:52.242 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:32:52.243 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:32:52.250 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.91 ms, Average inference time: 7.13 ms

2025-08-28 14:32:52.251 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:32:52.330 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:32:52.413 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch374
2025-08-28 14:32:55.195 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 374/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.138s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 6.263e-04, size: 384, ETA: 1:15:22
2025-08-28 14:32:58.192 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 374/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 6.255e-04, size: 416, ETA: 1:15:18
2025-08-28 14:33:01.149 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 374/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 6.248e-04, size: 384, ETA: 1:15:15
2025-08-28 14:33:04.129 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 374/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 6.240e-04, size: 512, ETA: 1:15:12
2025-08-28 14:33:07.314 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 374/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.3, l1_loss: 1.6, conf_loss: 3.1, cls_loss: 0.8, lr: 6.232e-04, size: 480, ETA: 1:15:09
2025-08-28 14:33:10.321 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 374/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.9, lr: 6.225e-04, size: 320, ETA: 1:15:06
2025-08-28 14:33:11.636 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:33:17.820 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:33:18.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:33:18.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5823
2025-08-28 14:33:18.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4935
2025-08-28 14:33:18.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3896
2025-08-28 14:33:18.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4885
2025-08-28 14:33:18.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:33:18.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:33:18.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-08-28 14:33:18.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-08-28 14:33:18.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-28 14:33:18.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-08-28 14:33:18.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:33:18.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:33:18.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:33:18.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:33:18.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:33:18.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:33:18.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:33:18.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:33:18.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:33:19.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:33:19.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:33:20.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:33:21.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:33:21.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:33:22.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:33:22.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:33:23.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:33:23.809 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:33:23.809 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:33:23.809 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:33:23.810 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:33:23.818 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.87 ms, Average inference time: 7.00 ms

2025-08-28 14:33:23.819 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:33:23.952 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:33:24.023 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch375
2025-08-28 14:33:26.958 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 375/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.7, lr: 6.214e-04, size: 320, ETA: 1:15:01
2025-08-28 14:33:29.994 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 375/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.5, cls_loss: 0.8, lr: 6.207e-04, size: 256, ETA: 1:14:58
2025-08-28 14:33:33.044 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 375/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.9, lr: 6.199e-04, size: 320, ETA: 1:14:55
2025-08-28 14:33:36.127 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 375/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 6.192e-04, size: 576, ETA: 1:14:52
2025-08-28 14:33:39.205 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 375/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 6.184e-04, size: 288, ETA: 1:14:49
2025-08-28 14:33:42.115 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 375/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 6.177e-04, size: 480, ETA: 1:14:45
2025-08-28 14:33:43.477 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:33:49.535 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:33:50.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:33:51.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5840
2025-08-28 14:33:51.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4883
2025-08-28 14:33:51.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3681
2025-08-28 14:33:51.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4801
2025-08-28 14:33:51.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:33:51.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:33:51.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-08-28 14:33:51.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-08-28 14:33:51.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-28 14:33:51.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.480
2025-08-28 14:33:51.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:33:51.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:33:51.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:33:51.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:33:51.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:33:51.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:33:51.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:33:51.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:33:51.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:33:52.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:33:52.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:33:53.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:33:54.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:33:55.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:33:55.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:33:56.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:33:57.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:33:58.064 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:33:58.065 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:33:58.065 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:33:58.065 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:33:58.072 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.96 ms, Average inference time: 7.18 ms

2025-08-28 14:33:58.073 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:33:58.154 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:33:58.234 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch376
2025-08-28 14:34:01.011 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 376/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.137s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 6.166e-04, size: 448, ETA: 1:14:41
2025-08-28 14:34:04.065 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 376/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.3, cls_loss: 0.7, lr: 6.158e-04, size: 416, ETA: 1:14:38
2025-08-28 14:34:07.137 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 376/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.5, lr: 6.151e-04, size: 576, ETA: 1:14:34
2025-08-28 14:34:10.204 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 376/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.1, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 3.8, cls_loss: 0.7, lr: 6.143e-04, size: 352, ETA: 1:14:31
2025-08-28 14:34:13.288 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 376/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 6.136e-04, size: 256, ETA: 1:14:28
2025-08-28 14:34:16.304 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 376/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.9, lr: 6.128e-04, size: 544, ETA: 1:14:25
2025-08-28 14:34:17.717 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:34:23.705 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:34:24.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:34:24.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5674
2025-08-28 14:34:24.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5029
2025-08-28 14:34:24.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3725
2025-08-28 14:34:24.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4809
2025-08-28 14:34:24.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:34:24.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:34:24.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.567
2025-08-28 14:34:24.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-08-28 14:34:24.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-28 14:34:24.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-08-28 14:34:24.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:34:24.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:34:24.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:34:24.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:34:24.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:34:24.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:34:24.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:34:24.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:34:24.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:34:25.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:34:25.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:34:26.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:34:26.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:34:27.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:34:27.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:34:28.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:34:28.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:34:29.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:34:29.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:34:29.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:34:29.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:34:29.147 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.93 ms, Average inference time: 7.02 ms

2025-08-28 14:34:29.149 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:34:29.227 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:34:29.305 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch377
2025-08-28 14:34:32.217 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 377/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 6.117e-04, size: 576, ETA: 1:14:20
2025-08-28 14:34:35.540 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 377/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.6, lr: 6.110e-04, size: 576, ETA: 1:14:17
2025-08-28 14:34:38.722 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 377/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.8, lr: 6.102e-04, size: 480, ETA: 1:14:14
2025-08-28 14:34:41.914 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 377/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 3.1, cls_loss: 0.7, lr: 6.095e-04, size: 256, ETA: 1:14:11
2025-08-28 14:34:44.813 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 377/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.7, lr: 6.087e-04, size: 320, ETA: 1:14:08
2025-08-28 14:34:47.808 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 377/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 6.080e-04, size: 352, ETA: 1:14:05
2025-08-28 14:34:49.225 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:34:55.331 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:34:56.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:34:56.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5801
2025-08-28 14:34:56.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5178
2025-08-28 14:34:56.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3537
2025-08-28 14:34:56.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4839
2025-08-28 14:34:56.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:34:56.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:34:56.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.580
2025-08-28 14:34:56.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.518
2025-08-28 14:34:56.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.354
2025-08-28 14:34:56.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-08-28 14:34:56.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:34:56.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:34:56.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:34:56.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:34:56.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:34:56.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:34:56.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:34:56.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:34:56.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:34:57.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:34:58.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:34:58.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:34:59.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:35:00.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:35:00.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:35:01.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:35:02.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:35:02.738 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:35:02.738 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:35:02.738 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:35:02.738 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:35:02.745 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.91 ms, Average inference time: 7.00 ms

2025-08-28 14:35:02.746 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:35:02.836 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:35:02.917 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch378
2025-08-28 14:35:05.950 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 378/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 6.069e-04, size: 480, ETA: 1:14:00
2025-08-28 14:35:09.027 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 378/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.5, lr: 6.062e-04, size: 384, ETA: 1:13:57
2025-08-28 14:35:12.113 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 378/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 6.054e-04, size: 576, ETA: 1:13:54
2025-08-28 14:35:15.156 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 378/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 1.2, lr: 6.047e-04, size: 416, ETA: 1:13:51
2025-08-28 14:35:18.152 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 378/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 1.1, lr: 6.039e-04, size: 256, ETA: 1:13:48
2025-08-28 14:35:21.268 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 378/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 6.032e-04, size: 256, ETA: 1:13:45
2025-08-28 14:35:22.592 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:35:28.816 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:35:30.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:35:30.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5830
2025-08-28 14:35:31.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5074
2025-08-28 14:35:31.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3426
2025-08-28 14:35:31.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4777
2025-08-28 14:35:31.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:35:31.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:35:31.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.583
2025-08-28 14:35:31.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-08-28 14:35:31.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.343
2025-08-28 14:35:31.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-08-28 14:35:31.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:35:31.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:35:31.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:35:31.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:35:31.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:35:31.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:35:31.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:35:31.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:35:31.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:35:32.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:35:33.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:35:34.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:35:35.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:35:36.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:35:37.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:35:38.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:35:39.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:35:40.355 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:35:40.355 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:35:40.355 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:35:40.356 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:35:40.363 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.98 ms, Average inference time: 7.23 ms

2025-08-28 14:35:40.364 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:35:40.447 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:35:40.527 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch379
2025-08-28 14:35:43.439 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 379/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 2.7, cls_loss: 0.5, lr: 6.021e-04, size: 544, ETA: 1:13:40
2025-08-28 14:35:46.699 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 379/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.160s, data_time: 0.005s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.8, lr: 6.014e-04, size: 448, ETA: 1:13:37
2025-08-28 14:35:49.814 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 379/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.6, lr: 6.006e-04, size: 288, ETA: 1:13:34
2025-08-28 14:35:52.663 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 379/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 5.999e-04, size: 352, ETA: 1:13:31
2025-08-28 14:35:55.572 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 379/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 5.991e-04, size: 448, ETA: 1:13:27
2025-08-28 14:35:58.580 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 379/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 9.3, iou_loss: 3.3, l1_loss: 1.2, conf_loss: 3.9, cls_loss: 0.9, lr: 5.984e-04, size: 256, ETA: 1:13:24
2025-08-28 14:35:59.968 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:36:06.078 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:36:06.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:36:07.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5708
2025-08-28 14:36:07.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4938
2025-08-28 14:36:07.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3797
2025-08-28 14:36:07.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4814
2025-08-28 14:36:07.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:36:07.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:36:07.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.571
2025-08-28 14:36:07.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-08-28 14:36:07.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-08-28 14:36:07.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-08-28 14:36:07.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:36:07.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:36:07.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:36:07.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:36:07.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:36:07.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:36:07.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:36:07.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:36:07.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:36:08.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:36:08.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:36:09.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:36:10.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:36:10.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:36:11.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:36:11.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:36:12.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:36:13.252 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:36:13.253 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:36:13.253 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:36:13.253 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:36:13.260 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.91 ms, Average inference time: 7.02 ms

2025-08-28 14:36:13.261 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:36:13.341 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:36:13.424 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch380
2025-08-28 14:36:16.210 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 380/600, iter: 20/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.137s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 5.973e-04, size: 352, ETA: 1:13:20
2025-08-28 14:36:19.168 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 380/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 5.966e-04, size: 512, ETA: 1:13:16
2025-08-28 14:36:22.277 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 380/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.3, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.4, lr: 5.958e-04, size: 416, ETA: 1:13:13
2025-08-28 14:36:25.394 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 380/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 1.6, conf_loss: 2.3, cls_loss: 0.8, lr: 5.951e-04, size: 544, ETA: 1:13:10
2025-08-28 14:36:28.433 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 380/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.6, lr: 5.943e-04, size: 416, ETA: 1:13:07
2025-08-28 14:36:31.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 380/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 5.936e-04, size: 576, ETA: 1:13:04
2025-08-28 14:36:32.835 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:36:38.976 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:36:39.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:36:40.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5921
2025-08-28 14:36:40.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5204
2025-08-28 14:36:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3578
2025-08-28 14:36:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4901
2025-08-28 14:36:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:36:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:36:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-08-28 14:36:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-08-28 14:36:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-08-28 14:36:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-08-28 14:36:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:36:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:36:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:36:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:36:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:36:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:36:40.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:36:40.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:36:40.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:36:41.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:36:42.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:36:42.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:36:43.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:36:44.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:36:45.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:36:45.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:36:46.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:36:47.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:36:47.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:36:47.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:36:47.251 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:36:47.267 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.91 ms, Average inference time: 7.17 ms

2025-08-28 14:36:47.268 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:36:47.469 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:36:47.542 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch381
2025-08-28 14:36:50.398 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 381/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 2.8, cls_loss: 0.8, lr: 5.925e-04, size: 512, ETA: 1:12:59
2025-08-28 14:36:53.422 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 381/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 5.918e-04, size: 352, ETA: 1:12:56
2025-08-28 14:36:56.422 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 381/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 9.6, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 4.4, cls_loss: 1.2, lr: 5.910e-04, size: 544, ETA: 1:12:53
2025-08-28 14:36:59.484 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 381/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.8, lr: 5.903e-04, size: 480, ETA: 1:12:50
2025-08-28 14:37:02.454 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 381/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 3.6, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 1.4, cls_loss: 0.4, lr: 5.896e-04, size: 352, ETA: 1:12:47
2025-08-28 14:37:05.680 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 381/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 5.888e-04, size: 480, ETA: 1:12:44
2025-08-28 14:37:07.058 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:37:13.204 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:37:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:37:15.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5869
2025-08-28 14:37:15.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4989
2025-08-28 14:37:15.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3584
2025-08-28 14:37:15.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4814
2025-08-28 14:37:15.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:37:15.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:37:15.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.587
2025-08-28 14:37:15.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-08-28 14:37:15.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-08-28 14:37:15.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-08-28 14:37:15.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:37:15.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:37:15.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:37:15.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:37:15.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:37:15.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:37:15.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:37:15.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:37:15.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:37:16.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:37:17.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:37:18.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:37:19.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:37:20.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:37:21.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:37:22.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:37:24.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:37:25.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:37:25.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:37:25.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:37:25.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:37:25.113 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.95 ms, Average inference time: 7.28 ms

2025-08-28 14:37:25.114 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:37:25.200 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:37:25.281 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch382
2025-08-28 14:37:28.165 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 382/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 5.877e-04, size: 512, ETA: 1:12:39
2025-08-28 14:37:31.082 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 382/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.8, lr: 5.870e-04, size: 256, ETA: 1:12:36
2025-08-28 14:37:34.165 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 382/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 5.863e-04, size: 352, ETA: 1:12:33
2025-08-28 14:37:37.141 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 382/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.8, cls_loss: 1.9, lr: 5.855e-04, size: 320, ETA: 1:12:29
2025-08-28 14:37:40.199 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 382/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.8, cls_loss: 0.8, lr: 5.848e-04, size: 416, ETA: 1:12:26
2025-08-28 14:37:43.185 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 382/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 5.841e-04, size: 384, ETA: 1:12:23
2025-08-28 14:37:44.556 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:37:50.698 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:37:51.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:37:51.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5557
2025-08-28 14:37:51.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4740
2025-08-28 14:37:51.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3506
2025-08-28 14:37:51.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4601
2025-08-28 14:37:51.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:37:51.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:37:51.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.556
2025-08-28 14:37:51.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-28 14:37:51.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.351
2025-08-28 14:37:51.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.460
2025-08-28 14:37:51.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:37:51.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:37:51.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:37:51.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:37:51.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:37:51.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:37:51.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:37:51.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:37:51.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:37:52.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:37:52.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:37:53.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:37:53.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:37:54.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:37:54.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:37:55.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:37:55.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:37:56.248 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:37:56.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:37:56.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 14:37:56.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:37:56.256 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.90 ms, Average inference time: 7.09 ms

2025-08-28 14:37:56.256 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:37:56.334 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:37:56.411 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch383
2025-08-28 14:37:59.305 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 383/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 15.1, iou_loss: 4.1, l1_loss: 2.4, conf_loss: 7.7, cls_loss: 0.9, lr: 5.830e-04, size: 256, ETA: 1:12:18
2025-08-28 14:38:02.214 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 383/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.6, lr: 5.822e-04, size: 352, ETA: 1:12:15
2025-08-28 14:38:05.347 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 383/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 8.8, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 4.0, cls_loss: 0.7, lr: 5.815e-04, size: 480, ETA: 1:12:12
2025-08-28 14:38:08.337 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 383/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.7, lr: 5.808e-04, size: 384, ETA: 1:12:09
2025-08-28 14:38:11.316 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 383/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 5.800e-04, size: 512, ETA: 1:12:06
2025-08-28 14:38:14.309 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 383/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 5.793e-04, size: 352, ETA: 1:12:03
2025-08-28 14:38:15.617 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:38:21.795 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:38:22.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:38:23.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5375
2025-08-28 14:38:23.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4354
2025-08-28 14:38:23.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2865
2025-08-28 14:38:23.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4198
2025-08-28 14:38:23.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:38:23.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:38:23.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.538
2025-08-28 14:38:23.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.435
2025-08-28 14:38:23.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.286
2025-08-28 14:38:23.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.420
2025-08-28 14:38:23.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:38:23.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:38:23.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:38:23.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:38:23.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:38:23.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:38:23.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:38:23.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:38:23.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:38:24.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:38:25.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:38:25.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:38:26.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:38:27.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:38:28.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:38:29.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:38:29.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:38:30.810 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:38:30.811 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 14:38:30.811 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-08-28 14:38:30.811 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:38:30.818 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.94 ms, Average inference time: 7.11 ms

2025-08-28 14:38:30.819 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:38:30.907 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:38:30.987 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch384
2025-08-28 14:38:33.792 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 384/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 5.782e-04, size: 416, ETA: 1:11:58
2025-08-28 14:38:36.883 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 384/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.7, lr: 5.775e-04, size: 512, ETA: 1:11:55
2025-08-28 14:38:39.849 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 384/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 5.768e-04, size: 416, ETA: 1:11:52
2025-08-28 14:38:42.818 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 384/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 5.760e-04, size: 480, ETA: 1:11:48
2025-08-28 14:38:45.757 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 384/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 5.753e-04, size: 352, ETA: 1:11:45
2025-08-28 14:38:48.717 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 384/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.8, lr: 5.746e-04, size: 448, ETA: 1:11:42
2025-08-28 14:38:50.082 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:38:56.448 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:38:57.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:38:57.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5779
2025-08-28 14:38:57.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5052
2025-08-28 14:38:58.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4034
2025-08-28 14:38:58.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4955
2025-08-28 14:38:58.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:38:58.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:38:58.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-08-28 14:38:58.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-08-28 14:38:58.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-08-28 14:38:58.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.496
2025-08-28 14:38:58.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:38:58.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:38:58.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:38:58.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:38:58.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:38:58.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:38:58.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:38:58.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:38:58.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:38:58.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:38:59.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:39:00.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:39:01.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:39:01.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:39:02.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:39:03.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:39:03.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:39:04.592 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:39:04.592 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:39:04.593 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 14:39:04.593 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:39:04.599 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.93 ms, Average inference time: 7.21 ms

2025-08-28 14:39:04.601 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:39:04.683 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:39:04.763 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch385
2025-08-28 14:39:07.746 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 385/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 2.1, cls_loss: 0.7, lr: 5.735e-04, size: 576, ETA: 1:11:37
2025-08-28 14:39:10.874 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 385/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 5.728e-04, size: 448, ETA: 1:11:34
2025-08-28 14:39:13.941 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 385/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.5, lr: 5.720e-04, size: 480, ETA: 1:11:31
2025-08-28 14:39:16.957 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 385/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 5.713e-04, size: 544, ETA: 1:11:28
2025-08-28 14:39:19.907 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 385/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 11.2, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 11.2, cls_loss: 0.0, lr: 5.706e-04, size: 480, ETA: 1:11:25
2025-08-28 14:39:23.010 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 385/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 5.698e-04, size: 480, ETA: 1:11:22
2025-08-28 14:39:24.410 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:39:30.486 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:39:31.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:39:31.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5698
2025-08-28 14:39:31.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5033
2025-08-28 14:39:31.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3629
2025-08-28 14:39:31.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4787
2025-08-28 14:39:31.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:39:31.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:39:31.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.570
2025-08-28 14:39:31.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-08-28 14:39:31.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-08-28 14:39:31.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.479
2025-08-28 14:39:31.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:39:31.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:39:31.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:39:31.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:39:31.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:39:31.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:39:31.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:39:31.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:39:31.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:39:32.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:39:32.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:39:33.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:39:33.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:39:34.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:39:34.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:39:35.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:39:35.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:39:36.438 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:39:36.438 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:39:36.438 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:39:36.438 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:39:36.445 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.90 ms, Average inference time: 7.12 ms

2025-08-28 14:39:36.446 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:39:36.581 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:39:36.653 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch386
2025-08-28 14:39:39.487 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 386/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 5.688e-04, size: 416, ETA: 1:11:17
2025-08-28 14:39:42.599 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 386/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 3.8, iou_loss: 1.3, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.5, lr: 5.680e-04, size: 352, ETA: 1:11:14
2025-08-28 14:39:45.754 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 386/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.2, cls_loss: 0.6, lr: 5.673e-04, size: 544, ETA: 1:11:11
2025-08-28 14:39:48.742 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 386/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 8.8, iou_loss: 3.8, l1_loss: 1.6, conf_loss: 2.4, cls_loss: 0.9, lr: 5.666e-04, size: 352, ETA: 1:11:08
2025-08-28 14:39:52.028 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 386/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 1.0, lr: 5.658e-04, size: 576, ETA: 1:11:05
2025-08-28 14:39:55.124 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 386/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.9, lr: 5.651e-04, size: 288, ETA: 1:11:02
2025-08-28 14:39:56.457 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:40:02.574 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:40:03.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:40:04.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5871
2025-08-28 14:40:04.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5004
2025-08-28 14:40:04.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3807
2025-08-28 14:40:04.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4894
2025-08-28 14:40:04.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:40:04.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:40:04.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.587
2025-08-28 14:40:04.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-08-28 14:40:04.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-08-28 14:40:04.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.489
2025-08-28 14:40:04.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:40:04.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:40:04.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:40:04.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:40:04.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:40:04.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:40:04.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:40:04.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:40:04.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:40:05.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:40:05.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:40:06.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:40:07.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:40:08.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:40:08.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:40:09.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:40:10.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:40:11.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:40:11.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:40:11.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:40:11.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:40:11.079 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.90 ms, Average inference time: 7.00 ms

2025-08-28 14:40:11.080 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:40:11.162 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:40:11.241 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch387
2025-08-28 14:40:14.135 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 387/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 5.640e-04, size: 544, ETA: 1:10:57
2025-08-28 14:40:17.181 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 387/600, iter: 40/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 5.633e-04, size: 352, ETA: 1:10:54
2025-08-28 14:40:20.227 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 387/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 5.626e-04, size: 416, ETA: 1:10:51
2025-08-28 14:40:23.245 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 387/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 2.9, iou_loss: 1.2, l1_loss: 0.4, conf_loss: 0.9, cls_loss: 0.4, lr: 5.618e-04, size: 448, ETA: 1:10:48
2025-08-28 14:40:26.197 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 387/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 5.611e-04, size: 384, ETA: 1:10:44
2025-08-28 14:40:29.264 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 387/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.8, lr: 5.604e-04, size: 256, ETA: 1:10:41
2025-08-28 14:40:30.691 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:40:36.735 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:40:37.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:40:38.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5768
2025-08-28 14:40:38.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4925
2025-08-28 14:40:38.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4005
2025-08-28 14:40:38.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4900
2025-08-28 14:40:38.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:40:38.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:40:38.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.577
2025-08-28 14:40:38.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-08-28 14:40:38.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-08-28 14:40:38.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-08-28 14:40:38.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:40:38.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:40:38.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:40:38.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:40:38.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:40:38.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:40:38.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:40:38.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:40:38.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:40:39.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:40:39.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:40:40.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:40:41.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:40:42.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:40:42.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:40:43.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:40:44.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:40:45.214 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:40:45.215 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:40:45.215 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:40:45.215 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:40:45.222 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.95 ms, Average inference time: 7.18 ms

2025-08-28 14:40:45.223 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:40:45.303 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:40:45.385 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch388
2025-08-28 14:40:48.206 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 388/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 5.593e-04, size: 480, ETA: 1:10:37
2025-08-28 14:40:51.289 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 388/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 8.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 4.5, cls_loss: 0.7, lr: 5.586e-04, size: 256, ETA: 1:10:34
2025-08-28 14:40:54.194 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 388/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 5.579e-04, size: 320, ETA: 1:10:30
2025-08-28 14:40:57.111 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 388/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 5.571e-04, size: 512, ETA: 1:10:27
2025-08-28 14:41:00.093 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 388/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 8.6, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.7, cls_loss: 0.8, lr: 5.564e-04, size: 416, ETA: 1:10:24
2025-08-28 14:41:03.177 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 388/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 1.2, cls_loss: 0.5, lr: 5.557e-04, size: 352, ETA: 1:10:21
2025-08-28 14:41:04.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:41:10.674 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:41:11.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:41:12.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5736
2025-08-28 14:41:12.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5072
2025-08-28 14:41:12.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3471
2025-08-28 14:41:12.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4760
2025-08-28 14:41:12.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:41:12.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:41:12.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-08-28 14:41:12.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-08-28 14:41:12.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-08-28 14:41:12.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.476
2025-08-28 14:41:12.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:41:12.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:41:12.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:41:12.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:41:12.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:41:12.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:41:12.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:41:12.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:41:12.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:41:12.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:41:13.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:41:14.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:41:15.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:41:15.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:41:16.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:41:17.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:41:17.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:41:18.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:41:18.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:41:18.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:41:18.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:41:18.491 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.94 ms, Average inference time: 7.21 ms

2025-08-28 14:41:18.492 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:41:18.619 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:41:18.692 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch389
2025-08-28 14:41:21.537 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 389/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 5.546e-04, size: 576, ETA: 1:10:16
2025-08-28 14:41:24.736 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 389/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.8, lr: 5.539e-04, size: 416, ETA: 1:10:13
2025-08-28 14:41:27.773 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 389/600, iter: 60/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.7, lr: 5.532e-04, size: 320, ETA: 1:10:10
2025-08-28 14:41:30.770 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 389/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 5.525e-04, size: 256, ETA: 1:10:07
2025-08-28 14:41:33.649 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 389/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 5.517e-04, size: 288, ETA: 1:10:04
2025-08-28 14:41:36.756 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 389/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.8, lr: 5.510e-04, size: 480, ETA: 1:10:01
2025-08-28 14:41:38.082 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:41:44.169 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:41:44.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:41:44.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5357
2025-08-28 14:41:45.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4753
2025-08-28 14:41:45.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3575
2025-08-28 14:41:45.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4562
2025-08-28 14:41:45.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:41:45.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:41:45.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-08-28 14:41:45.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-08-28 14:41:45.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-08-28 14:41:45.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.456
2025-08-28 14:41:45.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:41:45.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:41:45.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:41:45.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:41:45.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:41:45.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:41:45.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:41:45.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:41:45.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:41:45.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:41:45.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:41:46.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:41:46.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:41:47.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:41:47.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:41:47.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:41:48.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:41:48.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:41:48.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 14:41:48.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 14:41:48.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:41:48.778 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.82 ms, Average inference time: 6.98 ms

2025-08-28 14:41:48.779 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:41:48.868 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:41:48.948 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch390
2025-08-28 14:41:51.952 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 390/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 5.500e-04, size: 480, ETA: 1:09:56
2025-08-28 14:41:55.069 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 390/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.5, lr: 5.492e-04, size: 480, ETA: 1:09:53
2025-08-28 14:41:58.053 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 390/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 1.6, cls_loss: 0.7, lr: 5.485e-04, size: 256, ETA: 1:09:50
2025-08-28 14:42:01.211 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 390/600, iter: 80/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 15.2, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 10.1, cls_loss: 0.8, lr: 5.478e-04, size: 512, ETA: 1:09:47
2025-08-28 14:42:04.288 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 390/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.7, lr: 5.471e-04, size: 576, ETA: 1:09:43
2025-08-28 14:42:07.377 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 390/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.6, cls_loss: 0.7, lr: 5.463e-04, size: 352, ETA: 1:09:40
2025-08-28 14:42:08.722 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:42:14.923 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:42:15.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:42:16.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5904
2025-08-28 14:42:16.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4693
2025-08-28 14:42:16.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3682
2025-08-28 14:42:16.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4759
2025-08-28 14:42:16.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:42:16.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:42:16.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.590
2025-08-28 14:42:16.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-08-28 14:42:16.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-28 14:42:16.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.476
2025-08-28 14:42:16.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:42:16.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:42:16.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:42:16.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:42:16.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:42:16.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:42:16.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:42:16.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:42:16.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:42:17.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:42:17.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:42:18.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:42:19.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:42:19.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:42:20.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:42:21.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:42:21.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:42:22.463 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:42:22.463 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:42:22.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:42:22.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:42:22.471 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.92 ms, Average inference time: 7.20 ms

2025-08-28 14:42:22.472 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:42:22.554 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:42:22.633 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch391
2025-08-28 14:42:25.423 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 391/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.8, lr: 5.453e-04, size: 448, ETA: 1:09:36
2025-08-28 14:42:28.685 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 391/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 5.446e-04, size: 448, ETA: 1:09:33
2025-08-28 14:42:31.708 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 391/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 1.1, lr: 5.438e-04, size: 256, ETA: 1:09:30
2025-08-28 14:42:34.650 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 391/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 5.431e-04, size: 384, ETA: 1:09:26
2025-08-28 14:42:37.750 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 391/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.9, l1_loss: 0.8, conf_loss: 3.4, cls_loss: 0.8, lr: 5.424e-04, size: 448, ETA: 1:09:23
2025-08-28 14:42:40.819 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 391/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 9.1, iou_loss: 2.3, l1_loss: 1.6, conf_loss: 3.7, cls_loss: 1.4, lr: 5.417e-04, size: 576, ETA: 1:09:20
2025-08-28 14:42:42.172 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:42:48.377 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:42:49.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:42:49.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5832
2025-08-28 14:42:49.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5256
2025-08-28 14:42:49.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3961
2025-08-28 14:42:49.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5016
2025-08-28 14:42:49.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:42:49.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:42:49.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.583
2025-08-28 14:42:49.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-08-28 14:42:49.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-08-28 14:42:49.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-08-28 14:42:49.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:42:49.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:42:49.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:42:49.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:42:49.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:42:49.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:42:49.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:42:49.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:42:49.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:42:50.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:42:51.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:42:51.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:42:52.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:42:53.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:42:53.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:42:54.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:42:55.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:42:55.739 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:42:55.739 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:42:55.739 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 14:42:55.739 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:42:55.746 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.96 ms, Average inference time: 7.24 ms

2025-08-28 14:42:55.747 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:42:55.869 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:42:55.943 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch392
2025-08-28 14:42:58.940 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 392/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.7, lr: 5.406e-04, size: 576, ETA: 1:09:16
2025-08-28 14:43:01.946 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 392/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.3, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 5.399e-04, size: 512, ETA: 1:09:12
2025-08-28 14:43:04.997 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 392/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.9, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.6, lr: 5.392e-04, size: 544, ETA: 1:09:09
2025-08-28 14:43:08.075 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 392/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 5.385e-04, size: 288, ETA: 1:09:06
2025-08-28 14:43:11.182 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 392/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 5.377e-04, size: 256, ETA: 1:09:03
2025-08-28 14:43:14.282 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 392/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 10.0, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 5.0, cls_loss: 0.7, lr: 5.370e-04, size: 576, ETA: 1:09:00
2025-08-28 14:43:15.669 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:43:21.959 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:43:22.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:43:23.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5882
2025-08-28 14:43:23.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5156
2025-08-28 14:43:23.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3802
2025-08-28 14:43:23.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4947
2025-08-28 14:43:23.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:43:23.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:43:23.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-08-28 14:43:23.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-08-28 14:43:23.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-08-28 14:43:23.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-08-28 14:43:23.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:43:23.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:43:23.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:43:23.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:43:23.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:43:23.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:43:23.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:43:23.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:43:23.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:43:24.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:43:25.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:43:26.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:43:26.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:43:27.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:43:28.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:43:29.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:43:30.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:43:30.928 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:43:30.929 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:43:30.929 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:43:30.929 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:43:30.936 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.90 ms, Average inference time: 7.13 ms

2025-08-28 14:43:30.937 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:43:31.014 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:43:31.096 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch393
2025-08-28 14:43:33.977 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 393/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 5.360e-04, size: 512, ETA: 1:08:55
2025-08-28 14:43:36.954 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 393/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 5.353e-04, size: 352, ETA: 1:08:52
2025-08-28 14:43:40.200 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 393/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 5.345e-04, size: 352, ETA: 1:08:49
2025-08-28 14:43:43.270 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 393/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 5.338e-04, size: 288, ETA: 1:08:46
2025-08-28 14:43:46.307 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 393/600, iter: 100/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 5.331e-04, size: 352, ETA: 1:08:43
2025-08-28 14:43:49.273 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 393/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 0.7, cls_loss: 0.5, lr: 5.324e-04, size: 320, ETA: 1:08:40
2025-08-28 14:43:50.611 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:43:56.844 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:43:57.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:43:58.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5848
2025-08-28 14:43:58.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5070
2025-08-28 14:43:58.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3925
2025-08-28 14:43:58.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4948
2025-08-28 14:43:58.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:43:58.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:43:58.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.585
2025-08-28 14:43:58.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-08-28 14:43:58.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-28 14:43:58.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-08-28 14:43:58.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:43:58.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:43:58.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:43:58.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:43:58.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:43:58.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:43:58.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:43:58.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:43:58.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:43:59.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:43:59.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:44:00.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:44:01.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:44:02.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:44:02.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:44:03.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:44:04.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:44:05.200 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:44:05.200 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:44:05.201 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:44:05.201 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:44:05.208 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.40 ms, Average NMS time: 0.93 ms, Average inference time: 7.33 ms

2025-08-28 14:44:05.209 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:44:05.345 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:44:05.418 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch394
2025-08-28 14:44:08.229 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 394/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.139s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 5.314e-04, size: 256, ETA: 1:08:35
2025-08-28 14:44:11.236 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 394/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.7, lr: 5.306e-04, size: 448, ETA: 1:08:32
2025-08-28 14:44:14.208 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 394/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 5.299e-04, size: 320, ETA: 1:08:29
2025-08-28 14:44:17.233 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 394/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.8, lr: 5.292e-04, size: 320, ETA: 1:08:26
2025-08-28 14:44:20.253 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 394/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 5.285e-04, size: 384, ETA: 1:08:22
2025-08-28 14:44:23.335 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 394/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.7, lr: 5.278e-04, size: 448, ETA: 1:08:19
2025-08-28 14:44:24.638 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:44:30.830 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:44:31.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:44:32.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5940
2025-08-28 14:44:32.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5311
2025-08-28 14:44:32.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3668
2025-08-28 14:44:32.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4973
2025-08-28 14:44:32.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:44:32.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:44:32.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.594
2025-08-28 14:44:32.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-08-28 14:44:32.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.367
2025-08-28 14:44:32.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-08-28 14:44:32.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:44:32.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:44:32.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:44:32.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:44:32.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:44:32.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:44:32.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:44:32.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:44:32.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:44:33.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:44:34.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:44:35.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:44:36.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:44:37.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:44:38.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:44:39.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:44:40.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:44:41.478 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:44:41.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:44:41.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 14:44:41.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:44:41.486 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.92 ms, Average inference time: 7.13 ms

2025-08-28 14:44:41.487 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:44:41.564 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:44:41.643 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch395
2025-08-28 14:44:44.531 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 395/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.005s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 5.267e-04, size: 448, ETA: 1:08:15
2025-08-28 14:44:47.518 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 395/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.7, lr: 5.260e-04, size: 448, ETA: 1:08:12
2025-08-28 14:44:50.563 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 395/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 5.253e-04, size: 320, ETA: 1:08:08
2025-08-28 14:44:53.566 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 395/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.7, lr: 5.246e-04, size: 512, ETA: 1:08:05
2025-08-28 14:44:56.535 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 395/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 5.239e-04, size: 352, ETA: 1:08:02
2025-08-28 14:44:59.452 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 395/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 5.232e-04, size: 352, ETA: 1:07:59
2025-08-28 14:45:00.878 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:45:06.926 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:45:07.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:45:07.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5415
2025-08-28 14:45:07.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4711
2025-08-28 14:45:08.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3191
2025-08-28 14:45:08.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4439
2025-08-28 14:45:08.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:45:08.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:45:08.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-08-28 14:45:08.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-08-28 14:45:08.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.319
2025-08-28 14:45:08.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.444
2025-08-28 14:45:08.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:45:08.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:45:08.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:45:08.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:45:08.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:45:08.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:45:08.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:45:08.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:45:08.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:45:08.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:45:08.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:45:09.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:45:09.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:45:10.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:45:10.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:45:11.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:45:11.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:45:12.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:45:12.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 14:45:12.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 14:45:12.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:45:12.490 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.90 ms, Average inference time: 7.04 ms

2025-08-28 14:45:12.492 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:45:12.578 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:45:12.661 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch396
2025-08-28 14:45:15.737 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 396/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 5.221e-04, size: 544, ETA: 1:07:54
2025-08-28 14:45:18.807 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 396/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 5.214e-04, size: 384, ETA: 1:07:51
2025-08-28 14:45:21.896 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 396/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 5.207e-04, size: 384, ETA: 1:07:48
2025-08-28 14:45:24.821 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 396/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 5.200e-04, size: 448, ETA: 1:07:45
2025-08-28 14:45:27.822 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 396/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 5.193e-04, size: 512, ETA: 1:07:42
2025-08-28 14:45:30.848 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 396/600, iter: 120/129, gpu mem: 1367Mb, mem: 44.9Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 1.8, cls_loss: 0.9, lr: 5.186e-04, size: 352, ETA: 1:07:39
2025-08-28 14:45:32.180 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:45:38.236 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:45:39.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:45:39.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5977
2025-08-28 14:45:39.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5005
2025-08-28 14:45:39.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3823
2025-08-28 14:45:39.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4935
2025-08-28 14:45:39.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:45:39.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:45:39.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-08-28 14:45:39.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-08-28 14:45:39.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-08-28 14:45:39.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-08-28 14:45:39.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:45:39.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:45:39.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:45:39.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:45:39.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:45:39.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:45:39.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:45:39.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:45:39.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:45:40.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:45:41.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:45:42.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:45:42.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:45:43.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:45:44.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:45:45.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:45:46.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:45:46.749 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:45:46.749 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:45:46.749 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:45:46.749 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:45:46.757 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.92 ms, Average inference time: 7.22 ms

2025-08-28 14:45:46.758 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:45:46.834 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:45:46.914 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch397
2025-08-28 14:45:49.913 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 397/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 5.175e-04, size: 256, ETA: 1:07:34
2025-08-28 14:45:52.963 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 397/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.5, lr: 5.168e-04, size: 512, ETA: 1:07:31
2025-08-28 14:45:56.121 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 397/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 5.161e-04, size: 448, ETA: 1:07:28
2025-08-28 14:45:59.024 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 397/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 5.154e-04, size: 416, ETA: 1:07:25
2025-08-28 14:46:02.153 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 397/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 5.147e-04, size: 256, ETA: 1:07:22
2025-08-28 14:46:05.098 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 397/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 1.7, cls_loss: 0.6, lr: 5.140e-04, size: 576, ETA: 1:07:18
2025-08-28 14:46:06.547 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:46:12.770 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:46:13.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:46:14.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5780
2025-08-28 14:46:14.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4967
2025-08-28 14:46:14.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3473
2025-08-28 14:46:14.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4740
2025-08-28 14:46:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:46:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:46:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-08-28 14:46:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-08-28 14:46:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-08-28 14:46:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.474
2025-08-28 14:46:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:46:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:46:14.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:46:14.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:46:14.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:46:14.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:46:14.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:46:14.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:46:14.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:46:15.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:46:16.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:46:16.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:46:17.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:46:18.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:46:19.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:46:19.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:46:20.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:46:21.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:46:21.407 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 14:46:21.407 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:46:21.407 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:46:21.414 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.94 ms, Average inference time: 7.09 ms

2025-08-28 14:46:21.415 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:46:21.497 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:46:21.578 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch398
2025-08-28 14:46:24.423 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 398/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 0.7, lr: 5.130e-04, size: 544, ETA: 1:07:14
2025-08-28 14:46:27.491 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 398/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 5.122e-04, size: 256, ETA: 1:07:11
2025-08-28 14:46:30.379 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 398/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 5.115e-04, size: 256, ETA: 1:07:08
2025-08-28 14:46:33.481 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 398/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.7, lr: 5.108e-04, size: 288, ETA: 1:07:04
2025-08-28 14:46:36.524 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 398/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 5.101e-04, size: 416, ETA: 1:07:01
2025-08-28 14:46:39.472 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 398/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 10.2, iou_loss: 3.2, l1_loss: 1.7, conf_loss: 4.0, cls_loss: 1.4, lr: 5.094e-04, size: 384, ETA: 1:06:58
2025-08-28 14:46:40.897 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:46:47.022 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:46:47.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:46:48.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5862
2025-08-28 14:46:48.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5197
2025-08-28 14:46:48.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4153
2025-08-28 14:46:48.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5071
2025-08-28 14:46:48.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:46:48.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:46:48.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-08-28 14:46:48.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-08-28 14:46:48.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-08-28 14:46:48.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.507
2025-08-28 14:46:48.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:46:48.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:46:48.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:46:48.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:46:48.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:46:48.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:46:48.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:46:48.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:46:48.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:46:49.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:46:50.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:46:51.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:46:51.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:46:52.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:46:53.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:46:54.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:46:54.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:46:55.560 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:46:55.561 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:46:55.561 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 14:46:55.561 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:46:55.568 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.92 ms, Average inference time: 7.12 ms

2025-08-28 14:46:55.570 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:46:55.701 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:46:55.773 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch399
2025-08-28 14:46:58.802 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 399/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 5.084e-04, size: 448, ETA: 1:06:54
2025-08-28 14:47:01.771 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 399/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.8, lr: 5.077e-04, size: 576, ETA: 1:06:50
2025-08-28 14:47:04.926 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 399/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 5.070e-04, size: 256, ETA: 1:06:47
2025-08-28 14:47:07.902 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 399/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 5.063e-04, size: 256, ETA: 1:06:44
2025-08-28 14:47:10.854 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 399/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 5.056e-04, size: 576, ETA: 1:06:41
2025-08-28 14:47:14.092 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 399/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.8, lr: 5.049e-04, size: 352, ETA: 1:06:38
2025-08-28 14:47:15.503 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:47:21.649 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:47:22.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:47:23.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5777
2025-08-28 14:47:23.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5135
2025-08-28 14:47:23.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3850
2025-08-28 14:47:23.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4920
2025-08-28 14:47:23.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:47:23.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:47:23.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-08-28 14:47:23.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-08-28 14:47:23.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-28 14:47:23.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-08-28 14:47:23.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:47:23.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:47:23.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:47:23.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:47:23.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:47:23.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:47:23.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:47:23.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:47:23.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:47:24.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:47:24.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:47:25.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:47:26.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:47:27.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:47:28.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:47:28.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:47:29.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:47:30.460 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:47:30.460 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:47:30.460 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:47:30.461 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:47:30.468 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.93 ms, Average inference time: 7.09 ms

2025-08-28 14:47:30.469 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:47:30.551 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:47:30.632 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch400
2025-08-28 14:47:33.543 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 400/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.8, lr: 5.038e-04, size: 384, ETA: 1:06:33
2025-08-28 14:47:36.512 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 400/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.8, lr: 5.031e-04, size: 448, ETA: 1:06:30
2025-08-28 14:47:39.584 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 400/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.8, lr: 5.024e-04, size: 320, ETA: 1:06:27
2025-08-28 14:47:42.565 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 400/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 5.017e-04, size: 416, ETA: 1:06:24
2025-08-28 14:47:45.510 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 400/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 5.010e-04, size: 352, ETA: 1:06:21
2025-08-28 14:47:48.427 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 400/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 3.6, cls_loss: 0.6, lr: 5.003e-04, size: 320, ETA: 1:06:18
2025-08-28 14:47:49.775 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:47:55.898 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:47:56.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:47:57.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5839
2025-08-28 14:47:57.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5107
2025-08-28 14:47:57.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3725
2025-08-28 14:47:57.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4890
2025-08-28 14:47:57.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:47:57.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:47:57.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-08-28 14:47:57.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-08-28 14:47:57.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.373
2025-08-28 14:47:57.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.489
2025-08-28 14:47:57.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:47:57.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:47:57.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:47:57.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:47:57.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:47:57.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:47:57.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:47:57.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:47:57.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:47:58.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:47:59.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:48:00.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:48:01.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:48:02.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:48:03.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:48:04.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:48:05.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:48:06.109 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:48:06.109 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:48:06.109 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:48:06.109 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:48:06.117 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.94 ms, Average inference time: 7.06 ms

2025-08-28 14:48:06.118 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:48:06.194 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:48:06.280 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch401
2025-08-28 14:48:09.216 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 401/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.003s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 4.993e-04, size: 448, ETA: 1:06:13
2025-08-28 14:48:12.207 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 401/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.6, lr: 4.986e-04, size: 480, ETA: 1:06:10
2025-08-28 14:48:15.228 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 401/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.8, lr: 4.979e-04, size: 544, ETA: 1:06:07
2025-08-28 14:48:18.210 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 401/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.8, lr: 4.972e-04, size: 448, ETA: 1:06:04
2025-08-28 14:48:21.144 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 401/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 4.965e-04, size: 416, ETA: 1:06:00
2025-08-28 14:48:24.160 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 401/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.6, lr: 4.958e-04, size: 288, ETA: 1:05:57
2025-08-28 14:48:25.458 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:48:31.626 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:48:32.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:48:32.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5519
2025-08-28 14:48:33.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4873
2025-08-28 14:48:33.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3718
2025-08-28 14:48:33.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4703
2025-08-28 14:48:33.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:48:33.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:48:33.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.552
2025-08-28 14:48:33.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-08-28 14:48:33.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-28 14:48:33.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.470
2025-08-28 14:48:33.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:48:33.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:48:33.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:48:33.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:48:33.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:48:33.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:48:33.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:48:33.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:48:33.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:48:33.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:48:34.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:48:35.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:48:35.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:48:36.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:48:37.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:48:37.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:48:38.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:48:39.220 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:48:39.220 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:48:39.220 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:48:39.221 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:48:39.227 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.91 ms, Average inference time: 7.09 ms

2025-08-28 14:48:39.229 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:48:39.310 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:48:39.392 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch402
2025-08-28 14:48:42.108 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 402/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.134s, data_time: 0.003s, total_loss: 3.8, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 4.948e-04, size: 320, ETA: 1:05:53
2025-08-28 14:48:45.120 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 402/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.8, lr: 4.941e-04, size: 320, ETA: 1:05:49
2025-08-28 14:48:48.098 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 402/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.5, cls_loss: 0.5, lr: 4.934e-04, size: 416, ETA: 1:05:46
2025-08-28 14:48:51.134 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 402/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.6, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.5, lr: 4.927e-04, size: 416, ETA: 1:05:43
2025-08-28 14:48:54.243 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 402/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 4.920e-04, size: 384, ETA: 1:05:40
2025-08-28 14:48:57.261 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 402/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.6, cls_loss: 0.8, lr: 4.913e-04, size: 256, ETA: 1:05:37
2025-08-28 14:48:58.684 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:49:04.913 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:49:05.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:49:06.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5684
2025-08-28 14:49:06.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4740
2025-08-28 14:49:06.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3335
2025-08-28 14:49:06.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4586
2025-08-28 14:49:06.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:49:06.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:49:06.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.568
2025-08-28 14:49:06.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-08-28 14:49:06.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-08-28 14:49:06.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.459
2025-08-28 14:49:06.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:49:06.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:49:06.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:49:06.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:49:06.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:49:06.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:49:06.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:49:06.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:49:06.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:49:07.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:49:08.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:49:09.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:49:09.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:49:10.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:49:11.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:49:12.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:49:13.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:49:13.814 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:49:13.814 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:49:13.814 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 14:49:13.814 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:49:13.821 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.91 ms, Average inference time: 7.13 ms

2025-08-28 14:49:13.822 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:49:13.899 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:49:13.979 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch403
2025-08-28 14:49:16.949 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 403/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 4.903e-04, size: 544, ETA: 1:05:32
2025-08-28 14:49:20.051 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 403/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 4.896e-04, size: 416, ETA: 1:05:29
2025-08-28 14:49:23.050 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 403/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 4.889e-04, size: 256, ETA: 1:05:26
2025-08-28 14:49:25.964 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 403/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 4.882e-04, size: 416, ETA: 1:05:23
2025-08-28 14:49:28.959 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 403/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 1.1, lr: 4.875e-04, size: 352, ETA: 1:05:20
2025-08-28 14:49:31.911 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 403/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.9, lr: 4.868e-04, size: 320, ETA: 1:05:17
2025-08-28 14:49:33.235 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:49:39.340 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:49:40.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:49:41.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5767
2025-08-28 14:49:41.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5050
2025-08-28 14:49:41.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3624
2025-08-28 14:49:41.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4814
2025-08-28 14:49:41.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:49:41.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:49:41.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.577
2025-08-28 14:49:41.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-08-28 14:49:41.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-08-28 14:49:41.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-08-28 14:49:41.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:49:41.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:49:41.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:49:41.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:49:41.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:49:41.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:49:41.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:49:41.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:49:41.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:49:42.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:49:43.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:49:44.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:49:46.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:49:47.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:49:48.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:49:49.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:49:50.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:49:51.507 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:49:51.508 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 14:49:51.508 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:49:51.508 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:49:51.515 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.92 ms, Average inference time: 7.06 ms

2025-08-28 14:49:51.516 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:49:51.599 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:49:51.681 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch404
2025-08-28 14:49:54.737 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 404/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 4.858e-04, size: 288, ETA: 1:05:12
2025-08-28 14:49:57.751 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 404/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.7, lr: 4.851e-04, size: 544, ETA: 1:05:09
2025-08-28 14:50:00.762 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 404/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.6, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.5, lr: 4.844e-04, size: 480, ETA: 1:05:06
2025-08-28 14:50:03.753 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 404/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 3.1, cls_loss: 0.7, lr: 4.837e-04, size: 416, ETA: 1:05:03
2025-08-28 14:50:06.761 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 404/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.6, lr: 4.830e-04, size: 544, ETA: 1:04:59
2025-08-28 14:50:09.754 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 404/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 4.823e-04, size: 288, ETA: 1:04:56
2025-08-28 14:50:11.074 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:50:17.195 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:50:18.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:50:18.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5818
2025-08-28 14:50:18.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5098
2025-08-28 14:50:18.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3721
2025-08-28 14:50:18.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4879
2025-08-28 14:50:18.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:50:18.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:50:18.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-08-28 14:50:18.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.510
2025-08-28 14:50:18.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-28 14:50:18.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-08-28 14:50:18.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:50:18.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:50:18.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:50:18.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:50:18.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:50:18.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:50:18.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:50:18.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:50:18.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:50:19.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:50:20.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:50:21.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:50:21.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:50:22.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:50:23.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:50:23.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:50:24.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:50:25.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:50:25.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:50:25.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:50:25.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:50:25.438 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.93 ms, Average inference time: 7.04 ms

2025-08-28 14:50:25.438 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:50:25.523 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:50:25.604 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch405
2025-08-28 14:50:28.705 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 405/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.006s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.8, lr: 4.813e-04, size: 288, ETA: 1:04:52
2025-08-28 14:50:31.778 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 405/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 0.7, lr: 4.806e-04, size: 576, ETA: 1:04:49
2025-08-28 14:50:34.905 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 405/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 4.799e-04, size: 288, ETA: 1:04:46
2025-08-28 14:50:37.905 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 405/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 4.792e-04, size: 480, ETA: 1:04:42
2025-08-28 14:50:40.983 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 405/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 4.0, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 4.785e-04, size: 416, ETA: 1:04:39
2025-08-28 14:50:43.976 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 405/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.5, lr: 4.778e-04, size: 352, ETA: 1:04:36
2025-08-28 14:50:45.323 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:50:51.519 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:50:52.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:50:52.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5837
2025-08-28 14:50:52.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5430
2025-08-28 14:50:52.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3744
2025-08-28 14:50:52.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5003
2025-08-28 14:50:52.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:50:52.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:50:52.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-08-28 14:50:52.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-08-28 14:50:52.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.374
2025-08-28 14:50:52.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-08-28 14:50:52.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:50:52.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:50:52.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:50:52.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:50:53.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:50:54.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:50:54.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:50:55.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:50:56.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:50:56.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:50:57.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:50:58.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:50:58.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:50:58.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:50:58.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 14:50:58.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:50:58.769 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.92 ms, Average inference time: 7.07 ms

2025-08-28 14:50:58.769 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:50:58.901 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:50:58.974 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch406
2025-08-28 14:51:01.925 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 406/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 8.9, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 4.1, cls_loss: 0.8, lr: 4.768e-04, size: 544, ETA: 1:04:32
2025-08-28 14:51:04.998 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 406/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.0, l1_loss: 1.5, conf_loss: 3.8, cls_loss: 0.6, lr: 4.761e-04, size: 576, ETA: 1:04:29
2025-08-28 14:51:08.042 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 406/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 4.754e-04, size: 320, ETA: 1:04:25
2025-08-28 14:51:11.047 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 406/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 4.747e-04, size: 512, ETA: 1:04:22
2025-08-28 14:51:14.136 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 406/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.7, lr: 4.740e-04, size: 352, ETA: 1:04:19
2025-08-28 14:51:17.112 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 406/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.8, lr: 4.734e-04, size: 512, ETA: 1:04:16
2025-08-28 14:51:18.504 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:51:24.742 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:51:25.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:51:25.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5753
2025-08-28 14:51:25.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5300
2025-08-28 14:51:25.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3628
2025-08-28 14:51:25.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4893
2025-08-28 14:51:25.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:51:25.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:51:25.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.575
2025-08-28 14:51:25.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 14:51:25.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-08-28 14:51:25.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.489
2025-08-28 14:51:25.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:51:25.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:51:25.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:51:25.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:51:25.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:51:25.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:51:25.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:51:25.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:51:25.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:51:26.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:51:26.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:51:27.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:51:28.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:51:28.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:51:29.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:51:29.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:51:30.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:51:30.653 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:51:30.654 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:51:30.654 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:51:30.654 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:51:30.661 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.91 ms, Average inference time: 7.12 ms

2025-08-28 14:51:30.662 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:51:30.781 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:51:30.852 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch407
2025-08-28 14:51:33.780 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 407/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 4.724e-04, size: 320, ETA: 1:04:11
2025-08-28 14:51:36.942 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 407/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 4.717e-04, size: 352, ETA: 1:04:08
2025-08-28 14:51:39.970 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 407/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 4.710e-04, size: 512, ETA: 1:04:05
2025-08-28 14:51:42.999 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 407/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.5, lr: 4.703e-04, size: 352, ETA: 1:04:02
2025-08-28 14:51:45.925 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 407/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.3, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.5, lr: 4.696e-04, size: 480, ETA: 1:03:59
2025-08-28 14:51:48.929 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 407/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 9.4, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 4.0, cls_loss: 1.1, lr: 4.689e-04, size: 384, ETA: 1:03:56
2025-08-28 14:51:50.268 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:51:56.395 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:51:57.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:51:57.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5949
2025-08-28 14:51:57.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5159
2025-08-28 14:51:57.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3811
2025-08-28 14:51:57.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4973
2025-08-28 14:51:57.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:51:57.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:51:57.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.595
2025-08-28 14:51:57.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-08-28 14:51:57.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-08-28 14:51:57.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-08-28 14:51:57.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:51:57.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:51:57.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:51:57.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:51:57.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:51:57.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:51:57.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:51:57.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:51:57.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:51:58.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:51:59.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:52:00.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:52:00.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:52:01.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:52:02.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:52:02.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:52:03.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:52:04.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:52:04.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:52:04.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 14:52:04.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:52:04.410 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.34 ms, Average NMS time: 0.93 ms, Average inference time: 7.27 ms

2025-08-28 14:52:04.411 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:52:04.490 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:52:04.572 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch408
2025-08-28 14:52:07.466 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 408/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 4.679e-04, size: 288, ETA: 1:03:51
2025-08-28 14:52:10.446 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 408/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.9, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 7.9, cls_loss: 0.0, lr: 4.672e-04, size: 448, ETA: 1:03:48
2025-08-28 14:52:13.448 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 408/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 4.665e-04, size: 352, ETA: 1:03:45
2025-08-28 14:52:16.470 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 408/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 1.1, conf_loss: 1.3, cls_loss: 0.5, lr: 4.659e-04, size: 576, ETA: 1:03:42
2025-08-28 14:52:19.590 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 408/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 4.652e-04, size: 352, ETA: 1:03:39
2025-08-28 14:52:22.739 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 408/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 4.645e-04, size: 544, ETA: 1:03:36
2025-08-28 14:52:24.118 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:52:30.164 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:52:30.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:52:30.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5859
2025-08-28 14:52:31.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5253
2025-08-28 14:52:31.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3718
2025-08-28 14:52:31.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4943
2025-08-28 14:52:31.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:52:31.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:52:31.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-08-28 14:52:31.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 14:52:31.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-28 14:52:31.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-08-28 14:52:31.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:52:31.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:52:31.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:52:31.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:52:31.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:52:31.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:52:31.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:52:31.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:52:31.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:52:31.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:52:31.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:52:32.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:52:32.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:52:33.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:52:33.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:52:33.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:52:34.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:52:34.779 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:52:34.780 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:52:34.780 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:52:34.780 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:52:34.786 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.03 ms, Average NMS time: 0.86 ms, Average inference time: 6.89 ms

2025-08-28 14:52:34.788 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:52:34.872 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:52:34.949 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch409
2025-08-28 14:52:37.928 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 409/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 9.1, iou_loss: 3.0, l1_loss: 1.5, conf_loss: 3.8, cls_loss: 0.8, lr: 4.635e-04, size: 576, ETA: 1:03:31
2025-08-28 14:52:41.098 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 409/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 4.628e-04, size: 480, ETA: 1:03:28
2025-08-28 14:52:44.078 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 409/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.1, l1_loss: 0.5, conf_loss: 1.9, cls_loss: 0.4, lr: 4.621e-04, size: 448, ETA: 1:03:25
2025-08-28 14:52:47.331 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 409/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 2.0, cls_loss: 0.5, lr: 4.614e-04, size: 256, ETA: 1:03:22
2025-08-28 14:52:50.338 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 409/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 4.608e-04, size: 384, ETA: 1:03:19
2025-08-28 14:52:53.394 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 409/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.6, lr: 4.601e-04, size: 576, ETA: 1:03:16
2025-08-28 14:52:54.879 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:53:00.849 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:53:01.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:53:01.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5582
2025-08-28 14:53:01.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5042
2025-08-28 14:53:01.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3508
2025-08-28 14:53:01.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4711
2025-08-28 14:53:01.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:53:01.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:53:01.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.558
2025-08-28 14:53:01.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-08-28 14:53:01.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.351
2025-08-28 14:53:01.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.471
2025-08-28 14:53:01.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:53:01.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:53:01.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:53:01.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:53:01.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:53:01.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:53:01.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:53:01.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:53:01.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:53:02.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:53:02.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:53:03.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:53:03.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:53:04.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:53:04.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:53:05.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:53:05.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:53:05.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:53:05.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 14:53:05.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:53:05.887 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:53:05.898 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.86 ms, Average inference time: 7.04 ms

2025-08-28 14:53:05.901 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:53:06.020 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:53:06.148 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch410
2025-08-28 14:53:08.940 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 410/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.137s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 0.8, lr: 4.591e-04, size: 256, ETA: 1:03:11
2025-08-28 14:53:11.929 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 410/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 4.584e-04, size: 256, ETA: 1:03:08
2025-08-28 14:53:14.934 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 410/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.7, lr: 4.577e-04, size: 384, ETA: 1:03:05
2025-08-28 14:53:17.944 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 410/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.8, lr: 4.570e-04, size: 320, ETA: 1:03:02
2025-08-28 14:53:20.859 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 410/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 4.563e-04, size: 448, ETA: 1:02:58
2025-08-28 14:53:23.870 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 410/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 4.557e-04, size: 416, ETA: 1:02:55
2025-08-28 14:53:25.213 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:53:31.273 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:53:32.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:53:32.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5950
2025-08-28 14:53:32.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5307
2025-08-28 14:53:32.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4007
2025-08-28 14:53:32.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5088
2025-08-28 14:53:32.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:53:32.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:53:32.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.595
2025-08-28 14:53:32.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-08-28 14:53:32.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-08-28 14:53:32.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.509
2025-08-28 14:53:32.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:53:32.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:53:32.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:53:32.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:53:32.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:53:32.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:53:32.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:53:32.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:53:32.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:53:33.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:53:33.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:53:34.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:53:35.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:53:35.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:53:36.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:53:36.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:53:37.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:53:38.177 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:53:38.178 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:53:38.178 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 14:53:38.178 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:53:38.185 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.90 ms, Average inference time: 7.09 ms

2025-08-28 14:53:38.186 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:53:38.263 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:53:38.343 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch411
2025-08-28 14:53:41.201 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 411/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.141s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 4.547e-04, size: 384, ETA: 1:02:51
2025-08-28 14:53:44.134 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 411/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.8, lr: 4.540e-04, size: 384, ETA: 1:02:48
2025-08-28 14:53:47.171 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 411/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.5, lr: 4.533e-04, size: 544, ETA: 1:02:44
2025-08-28 14:53:50.116 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 411/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.6, lr: 4.526e-04, size: 256, ETA: 1:02:41
2025-08-28 14:53:53.265 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 411/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 4.520e-04, size: 320, ETA: 1:02:38
2025-08-28 14:53:56.194 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 411/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.8, lr: 4.513e-04, size: 320, ETA: 1:02:35
2025-08-28 14:53:57.502 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:54:03.751 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:54:04.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:54:05.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5736
2025-08-28 14:54:05.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5012
2025-08-28 14:54:06.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3955
2025-08-28 14:54:06.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4901
2025-08-28 14:54:06.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:54:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:54:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-08-28 14:54:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-08-28 14:54:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-08-28 14:54:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-08-28 14:54:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:54:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:54:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:54:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:54:06.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:54:06.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:54:06.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:54:06.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:54:06.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:54:07.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:54:08.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:54:09.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:54:10.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:54:11.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:54:12.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:54:13.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:54:14.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:54:15.340 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:54:15.340 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:54:15.340 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:54:15.340 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:54:15.348 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.36 ms, Average NMS time: 0.96 ms, Average inference time: 7.32 ms

2025-08-28 14:54:15.349 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:54:15.433 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:54:15.515 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch412
2025-08-28 14:54:18.459 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 412/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.9, lr: 4.503e-04, size: 288, ETA: 1:02:30
2025-08-28 14:54:21.503 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 412/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.9, lr: 4.496e-04, size: 512, ETA: 1:02:27
2025-08-28 14:54:24.570 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 412/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.0, iou_loss: 1.9, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.6, lr: 4.489e-04, size: 544, ETA: 1:02:24
2025-08-28 14:54:27.579 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 412/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.7, lr: 4.483e-04, size: 320, ETA: 1:02:21
2025-08-28 14:54:30.585 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 412/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 4.476e-04, size: 512, ETA: 1:02:18
2025-08-28 14:54:33.599 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 412/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.8, lr: 4.469e-04, size: 576, ETA: 1:02:15
2025-08-28 14:54:35.129 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:54:41.224 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:54:41.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:54:42.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5953
2025-08-28 14:54:42.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5055
2025-08-28 14:54:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3790
2025-08-28 14:54:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4933
2025-08-28 14:54:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:54:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:54:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.595
2025-08-28 14:54:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-08-28 14:54:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-08-28 14:54:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-08-28 14:54:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:54:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:54:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:54:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:54:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:54:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:54:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:54:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:54:42.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:54:43.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:54:43.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:54:44.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:54:44.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:54:45.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:54:46.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:54:46.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:54:47.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:54:48.024 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:54:48.024 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:54:48.024 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 14:54:48.024 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:54:48.031 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.90 ms, Average inference time: 7.06 ms

2025-08-28 14:54:48.036 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:54:48.115 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:54:48.194 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch413
2025-08-28 14:54:51.141 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 413/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.6, lr: 4.459e-04, size: 448, ETA: 1:02:10
2025-08-28 14:54:54.319 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 413/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 3.3, cls_loss: 0.7, lr: 4.453e-04, size: 448, ETA: 1:02:07
2025-08-28 14:54:57.309 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 413/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 4.446e-04, size: 352, ETA: 1:02:04
2025-08-28 14:55:00.241 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 413/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.7, lr: 4.439e-04, size: 288, ETA: 1:02:01
2025-08-28 14:55:03.251 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 413/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 4.432e-04, size: 544, ETA: 1:01:58
2025-08-28 14:55:06.331 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 413/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 4.426e-04, size: 544, ETA: 1:01:55
2025-08-28 14:55:07.646 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:55:13.878 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:55:14.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:55:15.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5736
2025-08-28 14:55:15.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4988
2025-08-28 14:55:15.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3520
2025-08-28 14:55:15.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4748
2025-08-28 14:55:15.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:55:15.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:55:15.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-08-28 14:55:15.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-08-28 14:55:15.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.352
2025-08-28 14:55:15.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.475
2025-08-28 14:55:15.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:55:15.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:55:15.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:55:15.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:55:15.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:55:15.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:55:15.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:55:15.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:55:15.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:55:16.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:55:17.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:55:18.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:55:19.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:55:20.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:55:21.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:55:22.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:55:23.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:55:24.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:55:24.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:55:24.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:55:24.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:55:24.653 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.36 ms, Average NMS time: 0.93 ms, Average inference time: 7.29 ms

2025-08-28 14:55:24.654 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:55:24.787 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:55:24.860 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch414
2025-08-28 14:55:27.870 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 414/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.6, lr: 4.416e-04, size: 416, ETA: 1:01:50
2025-08-28 14:55:30.861 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 414/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.6, lr: 4.409e-04, size: 416, ETA: 1:01:47
2025-08-28 14:55:33.940 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 414/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 4.402e-04, size: 480, ETA: 1:01:44
2025-08-28 14:55:37.123 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 414/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 0.6, lr: 4.396e-04, size: 480, ETA: 1:01:41
2025-08-28 14:55:40.249 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 414/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.6, lr: 4.389e-04, size: 576, ETA: 1:01:38
2025-08-28 14:55:43.329 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 414/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 4.382e-04, size: 448, ETA: 1:01:35
2025-08-28 14:55:44.680 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:55:50.668 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:55:51.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:55:52.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5850
2025-08-28 14:55:52.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5250
2025-08-28 14:55:52.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3753
2025-08-28 14:55:52.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4951
2025-08-28 14:55:52.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:55:52.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:55:52.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.585
2025-08-28 14:55:52.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 14:55:52.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-08-28 14:55:52.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-08-28 14:55:52.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:55:52.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:55:52.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:55:52.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:55:52.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:55:52.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:55:52.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:55:52.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:55:52.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:55:52.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:55:53.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:55:54.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:55:55.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:55:55.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:55:56.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:55:57.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:55:57.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:55:58.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:55:58.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:55:58.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 14:55:58.496 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:55:58.502 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.95 ms, Average inference time: 7.13 ms

2025-08-28 14:55:58.503 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:55:58.579 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:55:58.660 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch415
2025-08-28 14:56:01.493 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 415/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 4.372e-04, size: 288, ETA: 1:01:30
2025-08-28 14:56:04.430 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 415/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 1.1, lr: 4.366e-04, size: 480, ETA: 1:01:27
2025-08-28 14:56:07.596 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 415/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 4.359e-04, size: 256, ETA: 1:01:24
2025-08-28 14:56:10.547 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 415/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.7, lr: 4.352e-04, size: 512, ETA: 1:01:21
2025-08-28 14:56:13.629 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 415/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 4.346e-04, size: 320, ETA: 1:01:18
2025-08-28 14:56:16.586 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 415/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 2.1, cls_loss: 0.7, lr: 4.339e-04, size: 512, ETA: 1:01:14
2025-08-28 14:56:17.900 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:56:24.055 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:56:24.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:56:25.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5788
2025-08-28 14:56:25.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4830
2025-08-28 14:56:25.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3750
2025-08-28 14:56:25.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4789
2025-08-28 14:56:25.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:56:25.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:56:25.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-08-28 14:56:25.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.483
2025-08-28 14:56:25.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-08-28 14:56:25.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.479
2025-08-28 14:56:25.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:56:25.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:56:25.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:56:25.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:56:25.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:56:25.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:56:25.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:56:25.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:56:25.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:56:26.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:56:27.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:56:27.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:56:28.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:56:29.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:56:29.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:56:30.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:56:31.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:56:31.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:56:31.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:56:31.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 14:56:31.851 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:56:31.858 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.94 ms, Average inference time: 7.12 ms

2025-08-28 14:56:31.859 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:56:31.945 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:56:32.027 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch416
2025-08-28 14:56:34.893 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 416/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 4.329e-04, size: 256, ETA: 1:01:10
2025-08-28 14:56:37.894 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 416/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.5, lr: 4.323e-04, size: 576, ETA: 1:01:07
2025-08-28 14:56:41.076 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 416/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 4.316e-04, size: 512, ETA: 1:01:04
2025-08-28 14:56:44.102 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 416/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 4.309e-04, size: 256, ETA: 1:01:01
2025-08-28 14:56:47.287 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 416/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 4.303e-04, size: 384, ETA: 1:00:57
2025-08-28 14:56:50.340 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 416/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 4.296e-04, size: 544, ETA: 1:00:54
2025-08-28 14:56:51.729 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:56:57.773 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:56:58.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:56:58.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5727
2025-08-28 14:56:58.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4967
2025-08-28 14:56:58.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4188
2025-08-28 14:56:58.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4960
2025-08-28 14:56:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:56:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:56:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.573
2025-08-28 14:56:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-08-28 14:56:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-08-28 14:56:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.496
2025-08-28 14:56:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:56:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:56:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:56:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:56:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:56:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:56:58.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:56:58.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:56:58.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:56:59.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:57:00.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:57:00.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:57:01.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:57:01.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:57:02.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:57:02.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:57:03.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:57:04.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:57:04.034 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:57:04.034 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 14:57:04.034 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:57:04.040 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.89 ms, Average inference time: 7.12 ms

2025-08-28 14:57:04.042 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:57:04.167 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:57:04.279 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch417
2025-08-28 14:57:07.192 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 417/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.003s, total_loss: 3.9, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 4.286e-04, size: 352, ETA: 1:00:50
2025-08-28 14:57:10.255 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 417/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 1.4, conf_loss: 1.8, cls_loss: 0.7, lr: 4.280e-04, size: 352, ETA: 1:00:47
2025-08-28 14:57:13.443 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 417/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.8, lr: 4.273e-04, size: 576, ETA: 1:00:44
2025-08-28 14:57:16.517 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 417/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.2, cls_loss: 0.7, lr: 4.266e-04, size: 288, ETA: 1:00:41
2025-08-28 14:57:19.519 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 417/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 4.260e-04, size: 384, ETA: 1:00:37
2025-08-28 14:57:22.527 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 417/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.9, lr: 4.253e-04, size: 448, ETA: 1:00:34
2025-08-28 14:57:23.914 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:57:30.108 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:57:30.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:57:31.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5543
2025-08-28 14:57:31.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4845
2025-08-28 14:57:31.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3624
2025-08-28 14:57:31.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4671
2025-08-28 14:57:31.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:57:31.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:57:31.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.554
2025-08-28 14:57:31.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-08-28 14:57:31.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-08-28 14:57:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.467
2025-08-28 14:57:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:57:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:57:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:57:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:57:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:57:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:57:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:57:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:57:31.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:57:32.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:57:33.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:57:33.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:57:34.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:57:35.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:57:35.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:57:36.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:57:37.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:57:38.023 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:57:38.024 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:57:38.024 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:57:38.024 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:57:38.033 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.98 ms, Average inference time: 7.19 ms

2025-08-28 14:57:38.033 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:57:38.141 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:57:38.217 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch418
2025-08-28 14:57:41.168 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 418/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.005s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 4.243e-04, size: 320, ETA: 1:00:30
2025-08-28 14:57:44.181 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 418/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 1.7, lr: 4.237e-04, size: 256, ETA: 1:00:27
2025-08-28 14:57:47.381 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 418/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.6, lr: 4.230e-04, size: 576, ETA: 1:00:24
2025-08-28 14:57:50.522 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 418/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.6, lr: 4.223e-04, size: 576, ETA: 1:00:20
2025-08-28 14:57:53.516 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 418/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.6, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 2.7, cls_loss: 0.5, lr: 4.217e-04, size: 544, ETA: 1:00:17
2025-08-28 14:57:56.570 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 418/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.5, lr: 4.210e-04, size: 576, ETA: 1:00:14
2025-08-28 14:57:58.134 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:58:04.251 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:58:04.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:58:05.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5821
2025-08-28 14:58:05.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5193
2025-08-28 14:58:05.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4059
2025-08-28 14:58:05.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5025
2025-08-28 14:58:05.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:58:05.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:58:05.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-08-28 14:58:05.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.519
2025-08-28 14:58:05.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-08-28 14:58:05.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-08-28 14:58:05.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:58:05.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:58:05.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:58:05.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:58:05.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:58:05.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:58:05.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:58:05.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:58:05.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:58:06.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:58:06.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:58:07.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:58:07.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:58:08.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:58:09.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:58:09.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:58:10.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:58:10.742 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:58:10.743 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 14:58:10.743 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 14:58:10.743 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:58:10.750 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.94 ms, Average inference time: 7.14 ms

2025-08-28 14:58:10.751 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:58:10.825 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:58:10.904 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch419
2025-08-28 14:58:13.708 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 419/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.138s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 4.201e-04, size: 256, ETA: 1:00:10
2025-08-28 14:58:16.758 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 419/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.8, lr: 4.194e-04, size: 448, ETA: 1:00:07
2025-08-28 14:58:19.892 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 419/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 0.9, cls_loss: 0.7, lr: 4.187e-04, size: 512, ETA: 1:00:03
2025-08-28 14:58:22.856 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 419/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.5, lr: 4.181e-04, size: 256, ETA: 1:00:00
2025-08-28 14:58:25.781 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 419/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 4.174e-04, size: 480, ETA: 0:59:57
2025-08-28 14:58:28.807 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 419/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.9, lr: 4.168e-04, size: 384, ETA: 0:59:54
2025-08-28 14:58:30.235 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:58:36.442 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:58:37.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:58:37.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5918
2025-08-28 14:58:37.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4952
2025-08-28 14:58:37.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4235
2025-08-28 14:58:37.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5035
2025-08-28 14:58:37.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:58:37.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:58:37.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-08-28 14:58:37.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-08-28 14:58:37.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-28 14:58:37.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-08-28 14:58:37.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:58:37.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:58:37.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:58:37.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:58:37.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:58:37.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:58:37.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:58:37.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:58:37.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:58:38.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:58:38.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:58:39.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:58:40.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:58:40.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:58:41.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:58:41.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:58:42.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:58:43.149 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:58:43.149 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 14:58:43.149 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 14:58:43.149 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:58:43.156 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.06 ms, Average NMS time: 0.91 ms, Average inference time: 6.97 ms

2025-08-28 14:58:43.157 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:58:43.240 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:58:43.321 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch420
2025-08-28 14:58:46.251 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 420/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.7, lr: 4.158e-04, size: 320, ETA: 0:59:50
2025-08-28 14:58:49.113 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 420/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 4.151e-04, size: 352, ETA: 0:59:46
2025-08-28 14:58:52.028 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 420/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 4.145e-04, size: 480, ETA: 0:59:43
2025-08-28 14:58:55.175 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 420/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.7, l1_loss: 0.7, conf_loss: 3.6, cls_loss: 0.8, lr: 4.138e-04, size: 480, ETA: 0:59:40
2025-08-28 14:58:58.216 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 420/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 4.132e-04, size: 448, ETA: 0:59:37
2025-08-28 14:59:01.168 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 420/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.4, lr: 4.125e-04, size: 384, ETA: 0:59:34
2025-08-28 14:59:02.492 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:59:08.746 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:59:09.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:59:10.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5894
2025-08-28 14:59:10.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5136
2025-08-28 14:59:10.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3970
2025-08-28 14:59:10.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5000
2025-08-28 14:59:10.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:59:10.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:59:10.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-08-28 14:59:10.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.514
2025-08-28 14:59:10.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-08-28 14:59:10.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-08-28 14:59:10.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:59:10.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:59:10.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:59:10.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:59:10.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:59:10.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:59:10.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:59:10.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:59:10.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:59:10.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:59:11.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:59:12.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:59:13.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:59:13.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:59:14.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:59:15.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:59:15.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:59:16.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:59:16.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 14:59:16.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 14:59:16.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:59:16.435 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.92 ms, Average inference time: 7.21 ms

2025-08-28 14:59:16.436 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:59:16.516 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:59:16.595 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch421
2025-08-28 14:59:19.502 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 421/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.8, lr: 4.116e-04, size: 448, ETA: 0:59:29
2025-08-28 14:59:22.628 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 421/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.8, lr: 4.109e-04, size: 256, ETA: 0:59:26
2025-08-28 14:59:25.565 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 421/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 0.9, cls_loss: 0.6, lr: 4.102e-04, size: 256, ETA: 0:59:23
2025-08-28 14:59:28.788 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 421/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 4.096e-04, size: 288, ETA: 0:59:20
2025-08-28 14:59:31.750 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 421/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.5, lr: 4.089e-04, size: 320, ETA: 0:59:17
2025-08-28 14:59:34.721 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 421/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 4.083e-04, size: 544, ETA: 0:59:14
2025-08-28 14:59:36.081 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:59:42.310 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 14:59:42.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 14:59:43.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5694
2025-08-28 14:59:43.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4903
2025-08-28 14:59:43.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3497
2025-08-28 14:59:43.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4698
2025-08-28 14:59:43.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 14:59:43.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 14:59:43.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.569
2025-08-28 14:59:43.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-28 14:59:43.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.350
2025-08-28 14:59:43.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.470
2025-08-28 14:59:43.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 14:59:43.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 14:59:43.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 14:59:43.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 14:59:43.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 14:59:43.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 14:59:43.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 14:59:43.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 14:59:43.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 14:59:43.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 14:59:43.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 14:59:44.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 14:59:44.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 14:59:44.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 14:59:45.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 14:59:45.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 14:59:45.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 14:59:46.388 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 14:59:46.389 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 14:59:46.389 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 14:59:46.389 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 14:59:46.395 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.87 ms, Average inference time: 7.03 ms

2025-08-28 14:59:46.396 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:59:46.480 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 14:59:46.606 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch422
2025-08-28 14:59:49.623 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 422/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.6, lr: 4.073e-04, size: 576, ETA: 0:59:09
2025-08-28 14:59:52.721 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 422/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.6, iou_loss: 1.9, l1_loss: 1.3, conf_loss: 1.8, cls_loss: 0.5, lr: 4.067e-04, size: 480, ETA: 0:59:06
2025-08-28 14:59:55.694 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 422/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.8, lr: 4.060e-04, size: 256, ETA: 0:59:03
2025-08-28 14:59:58.699 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 422/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 4.054e-04, size: 512, ETA: 0:59:00
2025-08-28 15:00:01.777 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 422/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.8, lr: 4.047e-04, size: 480, ETA: 0:58:57
2025-08-28 15:00:04.790 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 422/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 4.041e-04, size: 544, ETA: 0:58:54
2025-08-28 15:00:06.178 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:00:12.350 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:00:13.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:00:13.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5760
2025-08-28 15:00:13.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5267
2025-08-28 15:00:13.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4083
2025-08-28 15:00:13.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5037
2025-08-28 15:00:13.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:00:13.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:00:13.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-08-28 15:00:13.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-08-28 15:00:13.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.408
2025-08-28 15:00:13.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-08-28 15:00:13.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:00:13.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:00:13.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:00:13.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:00:13.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:00:13.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:00:13.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:00:13.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:00:13.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:00:14.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:00:14.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:00:15.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:00:16.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:00:16.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:00:17.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:00:17.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:00:18.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:00:18.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:00:18.977 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:00:18.977 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:00:18.977 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:00:18.984 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.89 ms, Average inference time: 7.04 ms

2025-08-28 15:00:18.985 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:00:19.063 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:00:19.144 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch423
2025-08-28 15:00:21.962 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 423/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 4.031e-04, size: 256, ETA: 0:58:49
2025-08-28 15:00:24.883 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 423/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 4.025e-04, size: 352, ETA: 0:58:46
2025-08-28 15:00:27.869 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 423/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 9.7, iou_loss: 3.4, l1_loss: 1.2, conf_loss: 4.3, cls_loss: 0.8, lr: 4.018e-04, size: 416, ETA: 0:58:43
2025-08-28 15:00:31.050 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 423/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 4.012e-04, size: 480, ETA: 0:58:40
2025-08-28 15:00:34.082 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 423/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.6, lr: 4.005e-04, size: 320, ETA: 0:58:37
2025-08-28 15:00:37.022 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 423/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 3.999e-04, size: 320, ETA: 0:58:33
2025-08-28 15:00:38.429 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:00:44.663 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:00:45.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:00:45.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5763
2025-08-28 15:00:45.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4684
2025-08-28 15:00:45.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3467
2025-08-28 15:00:45.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4638
2025-08-28 15:00:45.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:00:45.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:00:45.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-08-28 15:00:45.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-08-28 15:00:45.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-08-28 15:00:45.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.464
2025-08-28 15:00:45.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:00:45.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:00:45.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:00:45.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:00:45.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:00:45.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:00:45.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:00:45.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:00:45.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:00:46.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:00:46.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:00:46.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:00:47.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:00:47.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:00:48.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:00:48.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:00:48.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:00:49.355 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:00:49.356 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:00:49.356 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 15:00:49.357 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:00:49.369 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.89 ms, Average inference time: 7.15 ms

2025-08-28 15:00:49.370 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:00:49.485 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:00:49.592 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch424
2025-08-28 15:00:52.487 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 424/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.003s, total_loss: 8.7, iou_loss: 3.2, l1_loss: 1.5, conf_loss: 3.2, cls_loss: 0.8, lr: 3.989e-04, size: 256, ETA: 0:58:29
2025-08-28 15:00:55.498 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 424/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 3.3, cls_loss: 1.0, lr: 3.983e-04, size: 320, ETA: 0:58:26
2025-08-28 15:00:58.542 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 424/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 3.976e-04, size: 256, ETA: 0:58:23
2025-08-28 15:01:01.541 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 424/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 3.970e-04, size: 256, ETA: 0:58:19
2025-08-28 15:01:04.604 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 424/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.8, lr: 3.963e-04, size: 544, ETA: 0:58:16
2025-08-28 15:01:07.715 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 424/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 3.957e-04, size: 384, ETA: 0:58:13
2025-08-28 15:01:09.059 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:01:15.093 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:01:15.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:01:16.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5701
2025-08-28 15:01:16.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4554
2025-08-28 15:01:16.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3606
2025-08-28 15:01:16.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4620
2025-08-28 15:01:16.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:01:16.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:01:16.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.570
2025-08-28 15:01:16.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.455
2025-08-28 15:01:16.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-08-28 15:01:16.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.462
2025-08-28 15:01:16.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:01:16.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:01:16.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:01:16.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:01:16.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:01:16.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:01:16.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:01:16.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:01:16.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:01:16.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:01:17.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:01:17.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:01:18.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:01:18.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:01:19.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:01:20.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:01:20.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:01:21.022 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:01:21.022 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 15:01:21.022 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 15:01:21.022 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:01:21.029 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.90 ms, Average inference time: 7.06 ms

2025-08-28 15:01:21.030 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:01:21.106 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:01:21.186 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch425
2025-08-28 15:01:24.076 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 425/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.003s, total_loss: 3.5, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 0.7, cls_loss: 0.5, lr: 3.948e-04, size: 416, ETA: 0:58:09
2025-08-28 15:01:27.150 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 425/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 13.8, iou_loss: 4.0, l1_loss: 2.2, conf_loss: 6.6, cls_loss: 0.9, lr: 3.941e-04, size: 288, ETA: 0:58:06
2025-08-28 15:01:30.130 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 425/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.7, lr: 3.935e-04, size: 416, ETA: 0:58:02
2025-08-28 15:01:33.235 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 425/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 3.928e-04, size: 288, ETA: 0:57:59
2025-08-28 15:01:36.357 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 425/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 3.922e-04, size: 416, ETA: 0:57:56
2025-08-28 15:01:39.258 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 425/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 3.915e-04, size: 448, ETA: 0:57:53
2025-08-28 15:01:40.624 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:01:46.750 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:01:47.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:01:47.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5769
2025-08-28 15:01:48.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5023
2025-08-28 15:01:48.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3651
2025-08-28 15:01:48.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4815
2025-08-28 15:01:48.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:01:48.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:01:48.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.577
2025-08-28 15:01:48.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-08-28 15:01:48.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-08-28 15:01:48.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-08-28 15:01:48.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:01:48.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:01:48.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:01:48.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:01:48.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:01:48.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:01:48.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:01:48.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:01:48.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:01:48.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:01:49.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:01:49.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:01:50.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:01:51.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:01:51.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:01:52.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:01:52.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:01:53.500 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:01:53.500 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 15:01:53.500 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 15:01:53.500 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:01:53.507 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.92 ms, Average inference time: 7.19 ms

2025-08-28 15:01:53.511 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:01:53.596 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:01:53.676 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch426
2025-08-28 15:01:56.576 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 426/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 3.906e-04, size: 384, ETA: 0:57:49
2025-08-28 15:01:59.478 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 426/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 3.900e-04, size: 448, ETA: 0:57:45
2025-08-28 15:02:02.484 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 426/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.6, lr: 3.893e-04, size: 544, ETA: 0:57:42
2025-08-28 15:02:05.521 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 426/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 0.8, cls_loss: 0.6, lr: 3.887e-04, size: 320, ETA: 0:57:39
2025-08-28 15:02:08.595 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 426/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.8, lr: 3.880e-04, size: 256, ETA: 0:57:36
2025-08-28 15:02:11.643 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 426/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 3.874e-04, size: 448, ETA: 0:57:33
2025-08-28 15:02:13.051 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:02:19.414 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:02:20.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:02:20.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5818
2025-08-28 15:02:20.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4987
2025-08-28 15:02:21.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3957
2025-08-28 15:02:21.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4921
2025-08-28 15:02:21.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:02:21.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:02:21.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-08-28 15:02:21.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-08-28 15:02:21.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-08-28 15:02:21.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-08-28 15:02:21.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:02:21.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:02:21.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:02:21.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:02:21.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:02:21.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:02:21.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:02:21.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:02:21.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:02:21.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:02:22.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:02:23.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:02:23.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:02:24.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:02:25.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:02:26.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:02:26.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:02:27.696 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:02:27.696 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:02:27.697 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:02:27.697 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:02:27.704 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.92 ms, Average inference time: 7.24 ms

2025-08-28 15:02:27.705 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:02:27.782 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:02:27.863 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch427
2025-08-28 15:02:30.753 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 427/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 10.6, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 5.6, cls_loss: 0.7, lr: 3.865e-04, size: 576, ETA: 0:57:28
2025-08-28 15:02:33.906 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 427/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.6, lr: 3.858e-04, size: 576, ETA: 0:57:25
2025-08-28 15:02:37.037 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 427/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.9, lr: 3.852e-04, size: 288, ETA: 0:57:22
2025-08-28 15:02:39.973 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 427/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.9, lr: 3.845e-04, size: 352, ETA: 0:57:19
2025-08-28 15:02:42.975 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 427/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 3.839e-04, size: 544, ETA: 0:57:16
2025-08-28 15:02:45.926 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 427/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.6, lr: 3.833e-04, size: 384, ETA: 0:57:13
2025-08-28 15:02:47.243 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:02:53.479 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:02:54.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:02:54.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5884
2025-08-28 15:02:55.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4640
2025-08-28 15:02:55.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3956
2025-08-28 15:02:55.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4827
2025-08-28 15:02:55.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:02:55.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:02:55.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-08-28 15:02:55.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.464
2025-08-28 15:02:55.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-08-28 15:02:55.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.483
2025-08-28 15:02:55.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:02:55.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:02:55.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:02:55.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:02:55.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:02:55.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:02:55.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:02:55.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:02:55.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:02:55.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:02:56.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:02:57.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:02:58.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:02:58.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:02:59.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:03:00.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:03:01.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:03:01.906 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:03:01.906 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 15:03:01.906 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 15:03:01.906 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:03:01.913 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.94 ms, Average inference time: 7.04 ms

2025-08-28 15:03:01.915 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:03:02.001 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:03:02.083 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch428
2025-08-28 15:03:05.062 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 428/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.6, lr: 3.823e-04, size: 416, ETA: 0:57:08
2025-08-28 15:03:08.194 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 428/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.8, lr: 3.817e-04, size: 512, ETA: 0:57:05
2025-08-28 15:03:11.180 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 428/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 3.810e-04, size: 288, ETA: 0:57:02
2025-08-28 15:03:14.238 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 428/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 3.804e-04, size: 416, ETA: 0:56:59
2025-08-28 15:03:17.347 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 428/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.7, lr: 3.798e-04, size: 512, ETA: 0:56:56
2025-08-28 15:03:20.449 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 428/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 3.791e-04, size: 480, ETA: 0:56:53
2025-08-28 15:03:21.857 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:03:28.023 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:03:28.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:03:29.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6041
2025-08-28 15:03:29.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5280
2025-08-28 15:03:29.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3866
2025-08-28 15:03:29.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5063
2025-08-28 15:03:29.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:03:29.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:03:29.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-08-28 15:03:29.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-08-28 15:03:29.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-28 15:03:29.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.506
2025-08-28 15:03:29.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:03:29.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:03:29.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:03:29.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:03:29.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:03:29.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:03:29.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:03:29.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:03:29.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:03:30.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:03:30.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:03:31.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:03:32.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:03:32.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:03:33.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:03:34.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:03:34.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:03:35.610 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:03:35.611 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:03:35.611 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:03:35.611 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:03:35.618 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.89 ms, Average inference time: 7.12 ms

2025-08-28 15:03:35.619 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:03:35.750 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:03:35.823 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch429
2025-08-28 15:03:38.733 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 429/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.7, lr: 3.782e-04, size: 352, ETA: 0:56:48
2025-08-28 15:03:41.769 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 429/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 3.776e-04, size: 384, ETA: 0:56:45
2025-08-28 15:03:44.729 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 429/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 3.769e-04, size: 416, ETA: 0:56:42
2025-08-28 15:03:47.681 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 429/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.3, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.5, lr: 3.763e-04, size: 448, ETA: 0:56:39
2025-08-28 15:03:50.674 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 429/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.5, lr: 3.757e-04, size: 384, ETA: 0:56:36
2025-08-28 15:03:53.614 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 429/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 3.750e-04, size: 352, ETA: 0:56:33
2025-08-28 15:03:54.980 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:04:01.086 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:04:01.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:04:02.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5754
2025-08-28 15:04:02.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5212
2025-08-28 15:04:02.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3687
2025-08-28 15:04:02.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4884
2025-08-28 15:04:02.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:04:02.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:04:02.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.575
2025-08-28 15:04:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-08-28 15:04:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-28 15:04:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-08-28 15:04:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:04:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:04:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:04:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:04:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:04:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:04:02.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:04:02.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:04:02.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:04:03.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:04:03.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:04:04.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:04:05.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:04:06.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:04:06.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:04:07.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:04:08.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:04:08.777 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:04:08.777 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:04:08.777 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:04:08.778 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:04:08.784 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.96 ms, Average inference time: 7.07 ms

2025-08-28 15:04:08.785 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:04:08.865 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:04:08.947 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch430
2025-08-28 15:04:11.797 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 430/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 3.741e-04, size: 288, ETA: 0:56:28
2025-08-28 15:04:14.977 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 430/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 3.735e-04, size: 512, ETA: 0:56:25
2025-08-28 15:04:18.033 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 430/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 1.1, lr: 3.729e-04, size: 288, ETA: 0:56:22
2025-08-28 15:04:21.186 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 430/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.4, cls_loss: 0.8, lr: 3.722e-04, size: 448, ETA: 0:56:19
2025-08-28 15:04:24.123 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 430/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.8, lr: 3.716e-04, size: 352, ETA: 0:56:16
2025-08-28 15:04:27.056 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 430/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.7, lr: 3.710e-04, size: 288, ETA: 0:56:13
2025-08-28 15:04:28.402 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:04:34.667 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:04:35.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:04:36.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5928
2025-08-28 15:04:36.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5273
2025-08-28 15:04:36.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4191
2025-08-28 15:04:36.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5131
2025-08-28 15:04:36.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:04:36.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:04:36.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-08-28 15:04:36.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-08-28 15:04:36.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-08-28 15:04:36.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-08-28 15:04:36.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:04:36.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:04:36.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:04:36.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:04:36.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:04:36.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:04:36.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:04:36.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:04:36.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:04:37.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:04:38.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:04:39.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:04:40.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:04:41.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:04:42.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:04:43.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:04:44.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:04:45.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:04:45.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:04:45.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:04:45.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:04:45.134 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.94 ms, Average inference time: 7.20 ms

2025-08-28 15:04:45.135 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:04:45.241 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:04:45.315 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch431
2025-08-28 15:04:48.211 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 431/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.5, lr: 3.700e-04, size: 576, ETA: 0:56:08
2025-08-28 15:04:51.250 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 431/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 1.2, cls_loss: 0.5, lr: 3.694e-04, size: 480, ETA: 0:56:05
2025-08-28 15:04:54.288 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 431/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 3.688e-04, size: 384, ETA: 0:56:02
2025-08-28 15:04:57.223 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 431/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.6, lr: 3.682e-04, size: 416, ETA: 0:55:59
2025-08-28 15:05:00.203 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 431/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 3.675e-04, size: 384, ETA: 0:55:55
2025-08-28 15:05:03.360 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 431/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.8, lr: 3.669e-04, size: 352, ETA: 0:55:52
2025-08-28 15:05:04.656 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:05:10.852 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:05:12.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:05:12.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5808
2025-08-28 15:05:13.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5042
2025-08-28 15:05:13.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3805
2025-08-28 15:05:13.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4885
2025-08-28 15:05:13.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:05:13.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:05:13.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.581
2025-08-28 15:05:13.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-08-28 15:05:13.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-08-28 15:05:13.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.489
2025-08-28 15:05:13.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:05:13.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:05:13.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:05:13.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:05:13.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:05:13.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:05:13.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:05:13.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:05:13.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:05:14.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:05:15.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:05:16.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:05:17.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:05:18.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:05:19.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:05:20.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:05:21.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:05:23.014 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:05:23.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:05:23.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:05:23.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:05:23.022 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.94 ms, Average inference time: 7.10 ms

2025-08-28 15:05:23.023 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:05:23.144 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:05:23.262 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch432
2025-08-28 15:05:26.126 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 432/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 3.660e-04, size: 384, ETA: 0:55:48
2025-08-28 15:05:29.094 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 432/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 3.654e-04, size: 288, ETA: 0:55:45
2025-08-28 15:05:32.302 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 432/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.5, lr: 3.647e-04, size: 544, ETA: 0:55:42
2025-08-28 15:05:35.241 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 432/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.8, cls_loss: 0.6, lr: 3.641e-04, size: 416, ETA: 0:55:39
2025-08-28 15:05:38.336 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 432/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.9, lr: 3.635e-04, size: 384, ETA: 0:55:35
2025-08-28 15:05:41.317 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 432/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 3.629e-04, size: 256, ETA: 0:55:32
2025-08-28 15:05:42.620 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:05:48.781 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:05:49.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:05:50.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5883
2025-08-28 15:05:50.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5265
2025-08-28 15:05:50.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3904
2025-08-28 15:05:50.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5017
2025-08-28 15:05:50.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:05:50.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:05:50.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-08-28 15:05:50.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-08-28 15:05:50.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-28 15:05:50.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-08-28 15:05:50.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:05:50.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:05:50.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:05:50.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:05:50.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:05:50.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:05:50.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:05:50.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:05:50.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:05:50.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:05:51.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:05:52.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:05:52.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:05:53.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:05:54.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:05:54.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:05:55.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:05:56.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:05:56.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:05:56.141 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:05:56.141 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:05:56.148 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.92 ms, Average inference time: 7.12 ms

2025-08-28 15:05:56.149 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:05:56.276 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:05:56.348 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch433
2025-08-28 15:05:59.320 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 433/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 3.620e-04, size: 576, ETA: 0:55:28
2025-08-28 15:06:02.395 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 433/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.8, lr: 3.613e-04, size: 256, ETA: 0:55:25
2025-08-28 15:06:05.407 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 433/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 3.607e-04, size: 384, ETA: 0:55:22
2025-08-28 15:06:08.427 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 433/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.5, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 3.5, cls_loss: 0.5, lr: 3.601e-04, size: 384, ETA: 0:55:18
2025-08-28 15:06:11.460 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 433/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 3.595e-04, size: 416, ETA: 0:55:15
2025-08-28 15:06:14.438 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 433/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.3, cls_loss: 0.6, lr: 3.588e-04, size: 448, ETA: 0:55:12
2025-08-28 15:06:15.775 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:06:21.763 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:06:22.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:06:22.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5332
2025-08-28 15:06:22.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4779
2025-08-28 15:06:22.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2987
2025-08-28 15:06:22.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4366
2025-08-28 15:06:22.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:06:22.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:06:22.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-08-28 15:06:22.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.478
2025-08-28 15:06:22.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.299
2025-08-28 15:06:22.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.437
2025-08-28 15:06:22.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:06:22.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:06:22.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:06:22.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:06:22.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:06:22.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:06:22.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:06:22.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:06:22.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:06:22.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:06:23.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:06:23.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:06:24.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:06:24.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:06:24.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:06:25.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:06:25.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:06:25.818 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:06:25.818 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-08-28 15:06:25.819 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-08-28 15:06:25.819 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:06:25.824 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.83 ms, Average inference time: 6.98 ms

2025-08-28 15:06:25.825 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:06:25.905 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:06:26.033 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch434
2025-08-28 15:06:29.041 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 434/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 3.4, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.5, lr: 3.579e-04, size: 416, ETA: 0:55:08
2025-08-28 15:06:32.275 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 434/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 3.573e-04, size: 512, ETA: 0:55:05
2025-08-28 15:06:35.264 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 434/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 3.0, iou_loss: 1.2, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.4, lr: 3.567e-04, size: 288, ETA: 0:55:01
2025-08-28 15:06:38.165 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 434/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 2.9, cls_loss: 0.6, lr: 3.561e-04, size: 448, ETA: 0:54:58
2025-08-28 15:06:41.305 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 434/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.0, cls_loss: 0.6, lr: 3.554e-04, size: 576, ETA: 0:54:55
2025-08-28 15:06:44.437 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 434/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.9, lr: 3.548e-04, size: 256, ETA: 0:54:52
2025-08-28 15:06:45.803 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:06:51.903 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:06:52.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:06:52.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5740
2025-08-28 15:06:52.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4896
2025-08-28 15:06:52.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4071
2025-08-28 15:06:52.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4902
2025-08-28 15:06:52.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:06:52.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:06:52.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-08-28 15:06:52.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-28 15:06:52.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-28 15:06:52.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-08-28 15:06:52.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:06:52.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:06:52.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:06:52.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:06:52.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:06:52.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:06:52.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:06:52.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:06:52.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:06:53.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:06:53.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:06:53.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:06:54.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:06:54.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:06:55.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:06:55.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:06:55.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:06:56.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:06:56.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:06:56.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:06:56.174 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:06:56.180 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.86 ms, Average inference time: 7.17 ms

2025-08-28 15:06:56.181 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:06:56.258 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:06:56.339 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch435
2025-08-28 15:06:59.166 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 435/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 3.539e-04, size: 352, ETA: 0:54:48
2025-08-28 15:07:02.144 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 435/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 3.533e-04, size: 320, ETA: 0:54:44
2025-08-28 15:07:05.266 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 435/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.5, lr: 3.527e-04, size: 544, ETA: 0:54:41
2025-08-28 15:07:08.300 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 435/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 1.6, conf_loss: 2.3, cls_loss: 0.7, lr: 3.521e-04, size: 416, ETA: 0:54:38
2025-08-28 15:07:11.302 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 435/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 3.514e-04, size: 512, ETA: 0:54:35
2025-08-28 15:07:14.337 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 435/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 3.2, cls_loss: 0.7, lr: 3.508e-04, size: 576, ETA: 0:54:32
2025-08-28 15:07:15.787 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:07:22.070 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:07:23.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:07:23.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5866
2025-08-28 15:07:23.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5102
2025-08-28 15:07:23.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3689
2025-08-28 15:07:23.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4886
2025-08-28 15:07:23.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:07:23.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:07:23.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.587
2025-08-28 15:07:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.510
2025-08-28 15:07:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-08-28 15:07:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.489
2025-08-28 15:07:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:07:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:07:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:07:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:07:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:07:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:07:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:07:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:07:23.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:07:24.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:07:25.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:07:26.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:07:27.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:07:28.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:07:29.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:07:29.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:07:30.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:07:31.554 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:07:31.554 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:07:31.554 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:07:31.555 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:07:31.562 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.92 ms, Average inference time: 7.11 ms

2025-08-28 15:07:31.563 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:07:31.702 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:07:31.776 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch436
2025-08-28 15:07:34.687 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 436/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 3.499e-04, size: 448, ETA: 0:54:28
2025-08-28 15:07:37.612 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 436/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 3.493e-04, size: 416, ETA: 0:54:24
2025-08-28 15:07:40.590 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 436/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 2.0, cls_loss: 0.5, lr: 3.487e-04, size: 416, ETA: 0:54:21
2025-08-28 15:07:43.488 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 436/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 3.481e-04, size: 384, ETA: 0:54:18
2025-08-28 15:07:46.558 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 436/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 3.475e-04, size: 544, ETA: 0:54:15
2025-08-28 15:07:49.595 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 436/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.5, lr: 3.469e-04, size: 320, ETA: 0:54:12
2025-08-28 15:07:50.975 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:07:57.087 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:07:57.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:07:57.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5476
2025-08-28 15:07:58.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4835
2025-08-28 15:07:58.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3680
2025-08-28 15:07:58.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4664
2025-08-28 15:07:58.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:07:58.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:07:58.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.548
2025-08-28 15:07:58.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-28 15:07:58.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-28 15:07:58.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.466
2025-08-28 15:07:58.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:07:58.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:07:58.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:07:58.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:07:58.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:07:58.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:07:58.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:07:58.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:07:58.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:07:58.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:07:58.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:07:59.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:07:59.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:08:00.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:08:00.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:08:00.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:08:01.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:08:01.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:08:01.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:08:01.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 15:08:01.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:08:01.673 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.85 ms, Average inference time: 7.10 ms

2025-08-28 15:08:01.674 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:08:01.756 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:08:01.834 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch437
2025-08-28 15:08:04.685 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 437/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.7, lr: 3.460e-04, size: 384, ETA: 0:54:07
2025-08-28 15:08:07.737 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 437/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 3.454e-04, size: 480, ETA: 0:54:04
2025-08-28 15:08:10.767 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 437/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.6, lr: 3.447e-04, size: 416, ETA: 0:54:01
2025-08-28 15:08:13.867 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 437/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 3.441e-04, size: 416, ETA: 0:53:58
2025-08-28 15:08:16.826 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 437/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 3.435e-04, size: 384, ETA: 0:53:55
2025-08-28 15:08:19.967 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 437/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.0, cls_loss: 0.6, lr: 3.429e-04, size: 512, ETA: 0:53:52
2025-08-28 15:08:21.359 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:08:27.628 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:08:28.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:08:29.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5690
2025-08-28 15:08:29.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4788
2025-08-28 15:08:29.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3264
2025-08-28 15:08:29.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4581
2025-08-28 15:08:29.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:08:29.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:08:29.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.569
2025-08-28 15:08:29.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-08-28 15:08:29.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.326
2025-08-28 15:08:29.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.458
2025-08-28 15:08:29.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:08:29.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:08:29.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:08:29.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:08:29.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:08:29.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:08:29.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:08:29.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:08:29.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:08:30.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:08:31.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:08:32.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:08:32.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:08:33.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:08:34.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:08:35.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:08:36.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:08:36.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:08:36.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 15:08:36.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 15:08:36.959 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:08:36.966 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.92 ms, Average inference time: 7.04 ms

2025-08-28 15:08:36.967 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:08:37.091 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:08:37.171 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch438
2025-08-28 15:08:40.112 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 438/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 9.7, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 4.6, cls_loss: 0.9, lr: 3.420e-04, size: 384, ETA: 0:53:47
2025-08-28 15:08:43.043 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 438/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 3.414e-04, size: 352, ETA: 0:53:44
2025-08-28 15:08:45.965 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 438/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.8, lr: 3.408e-04, size: 416, ETA: 0:53:41
2025-08-28 15:08:48.926 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 438/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.8, lr: 3.402e-04, size: 352, ETA: 0:53:38
2025-08-28 15:08:51.921 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 438/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.4, conf_loss: 2.3, cls_loss: 0.7, lr: 3.396e-04, size: 448, ETA: 0:53:35
2025-08-28 15:08:54.943 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 438/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.6, lr: 3.390e-04, size: 576, ETA: 0:53:32
2025-08-28 15:08:56.339 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:09:02.622 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:09:03.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:09:03.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5927
2025-08-28 15:09:03.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5239
2025-08-28 15:09:03.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3762
2025-08-28 15:09:03.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4976
2025-08-28 15:09:03.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:09:03.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:09:03.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-08-28 15:09:03.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-08-28 15:09:03.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-08-28 15:09:03.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.498
2025-08-28 15:09:03.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:09:03.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:09:03.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:09:03.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:09:03.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:09:03.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:09:03.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:09:03.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:09:03.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:09:04.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:09:05.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:09:05.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:09:06.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:09:06.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:09:07.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:09:07.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:09:08.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:09:08.914 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:09:08.914 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:09:08.914 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:09:08.914 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:09:08.927 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.92 ms, Average inference time: 7.16 ms

2025-08-28 15:09:08.928 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:09:09.042 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:09:09.115 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch439
2025-08-28 15:09:12.058 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 439/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.8, lr: 3.381e-04, size: 480, ETA: 0:53:27
2025-08-28 15:09:15.078 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 439/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.1, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.7, lr: 3.375e-04, size: 576, ETA: 0:53:24
2025-08-28 15:09:18.257 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 439/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 0.8, cls_loss: 0.6, lr: 3.369e-04, size: 320, ETA: 0:53:21
2025-08-28 15:09:21.164 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 439/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 3.363e-04, size: 320, ETA: 0:53:18
2025-08-28 15:09:24.112 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 439/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 3.356e-04, size: 416, ETA: 0:53:15
2025-08-28 15:09:27.087 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 439/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 3.350e-04, size: 288, ETA: 0:53:12
2025-08-28 15:09:28.488 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:09:34.794 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:09:35.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:09:36.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5649
2025-08-28 15:09:36.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4439
2025-08-28 15:09:36.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3553
2025-08-28 15:09:36.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4547
2025-08-28 15:09:36.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:09:36.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:09:36.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.565
2025-08-28 15:09:36.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-08-28 15:09:36.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.355
2025-08-28 15:09:36.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.455
2025-08-28 15:09:36.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:09:36.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:09:36.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:09:36.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:09:36.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:09:36.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:09:36.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:09:36.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:09:36.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:09:37.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:09:37.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:09:38.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:09:39.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:09:39.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:09:40.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:09:41.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:09:41.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:09:42.668 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:09:42.668 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 15:09:42.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 15:09:42.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:09:42.676 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.38 ms, Average NMS time: 0.94 ms, Average inference time: 7.32 ms

2025-08-28 15:09:42.677 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:09:42.758 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:09:42.839 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch440
2025-08-28 15:09:45.638 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 440/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.138s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.5, lr: 3.342e-04, size: 544, ETA: 0:53:07
2025-08-28 15:09:48.666 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 440/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.7, lr: 3.336e-04, size: 320, ETA: 0:53:04
2025-08-28 15:09:51.710 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 440/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.6, lr: 3.330e-04, size: 480, ETA: 0:53:01
2025-08-28 15:09:54.728 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 440/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.6, lr: 3.323e-04, size: 288, ETA: 0:52:58
2025-08-28 15:09:57.672 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 440/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.7, lr: 3.317e-04, size: 448, ETA: 0:52:55
2025-08-28 15:10:00.862 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 440/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 1.7, cls_loss: 0.6, lr: 3.311e-04, size: 576, ETA: 0:52:52
2025-08-28 15:10:02.289 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:10:08.395 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:10:09.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:10:09.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5933
2025-08-28 15:10:09.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4926
2025-08-28 15:10:09.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4037
2025-08-28 15:10:09.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4965
2025-08-28 15:10:09.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:10:09.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:10:09.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-08-28 15:10:09.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-08-28 15:10:09.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.404
2025-08-28 15:10:09.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-08-28 15:10:09.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:10:09.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:10:09.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:10:09.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:10:09.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:10:09.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:10:09.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:10:09.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:10:09.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:10:10.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:10:10.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:10:11.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:10:11.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:10:12.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:10:12.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:10:13.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:10:13.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:10:14.349 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:10:14.349 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:10:14.349 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:10:14.349 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:10:14.356 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.94 ms, Average inference time: 7.10 ms

2025-08-28 15:10:14.357 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:10:14.435 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:10:14.516 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch441
2025-08-28 15:10:17.338 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 441/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 3.303e-04, size: 416, ETA: 0:52:47
2025-08-28 15:10:20.434 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 441/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 3.297e-04, size: 352, ETA: 0:52:44
2025-08-28 15:10:23.528 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 441/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.8, lr: 3.291e-04, size: 320, ETA: 0:52:41
2025-08-28 15:10:26.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 441/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.6, lr: 3.285e-04, size: 256, ETA: 0:52:38
2025-08-28 15:10:29.522 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 441/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.0, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 1.2, lr: 3.279e-04, size: 416, ETA: 0:52:35
2025-08-28 15:10:32.590 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 441/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 3.273e-04, size: 480, ETA: 0:52:32
2025-08-28 15:10:34.040 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:10:40.395 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:10:41.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:10:41.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5679
2025-08-28 15:10:41.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5157
2025-08-28 15:10:41.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3867
2025-08-28 15:10:41.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4901
2025-08-28 15:10:41.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:10:41.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:10:41.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.568
2025-08-28 15:10:41.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-08-28 15:10:41.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-28 15:10:41.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-08-28 15:10:41.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:10:41.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:10:41.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:10:41.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:10:41.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:10:41.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:10:41.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:10:41.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:10:41.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:10:42.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:10:43.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:10:43.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:10:44.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:10:44.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:10:45.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:10:46.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:10:46.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:10:47.345 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:10:47.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:10:47.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:10:47.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:10:47.353 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.95 ms, Average inference time: 7.14 ms

2025-08-28 15:10:47.354 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:10:47.437 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:10:47.520 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch442
2025-08-28 15:10:50.419 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 442/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.8, lr: 3.264e-04, size: 288, ETA: 0:52:27
2025-08-28 15:10:53.419 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 442/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 3.258e-04, size: 416, ETA: 0:52:24
2025-08-28 15:10:56.309 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 442/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 3.252e-04, size: 288, ETA: 0:52:21
2025-08-28 15:10:59.398 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 442/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 3.246e-04, size: 384, ETA: 0:52:18
2025-08-28 15:11:02.384 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 442/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 3.240e-04, size: 512, ETA: 0:52:15
2025-08-28 15:11:05.439 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 442/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.6, lr: 3.234e-04, size: 512, ETA: 0:52:11
2025-08-28 15:11:06.860 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:11:13.166 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:11:13.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:11:14.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5935
2025-08-28 15:11:14.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5106
2025-08-28 15:11:14.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3761
2025-08-28 15:11:14.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4934
2025-08-28 15:11:14.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:11:14.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:11:14.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-08-28 15:11:14.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-08-28 15:11:14.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-08-28 15:11:14.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-08-28 15:11:14.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:11:14.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:11:14.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:11:14.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:11:14.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:11:14.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:11:14.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:11:14.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:11:14.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:11:15.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:11:15.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:11:16.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:11:16.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:11:17.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:11:18.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:11:18.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:11:19.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:11:19.891 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:11:19.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:11:19.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:11:19.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:11:19.899 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.33 ms, Average NMS time: 0.89 ms, Average inference time: 7.22 ms

2025-08-28 15:11:19.900 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:11:19.983 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:11:20.061 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch443
2025-08-28 15:11:22.959 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 443/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 3.225e-04, size: 352, ETA: 0:52:07
2025-08-28 15:11:26.218 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 443/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 3.5, cls_loss: 0.7, lr: 3.219e-04, size: 576, ETA: 0:52:04
2025-08-28 15:11:29.276 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 443/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 3.213e-04, size: 320, ETA: 0:52:01
2025-08-28 15:11:32.372 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 443/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 3.207e-04, size: 448, ETA: 0:51:58
2025-08-28 15:11:35.518 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 443/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 3.2, cls_loss: 0.6, lr: 3.201e-04, size: 448, ETA: 0:51:55
2025-08-28 15:11:38.689 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 443/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.157s, data_time: 0.006s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.7, lr: 3.195e-04, size: 576, ETA: 0:51:52
2025-08-28 15:11:40.057 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:11:46.217 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:11:47.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:11:47.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5842
2025-08-28 15:11:47.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4685
2025-08-28 15:11:48.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3580
2025-08-28 15:11:48.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4702
2025-08-28 15:11:48.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:11:48.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:11:48.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-08-28 15:11:48.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-08-28 15:11:48.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-08-28 15:11:48.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.470
2025-08-28 15:11:48.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:11:48.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:11:48.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:11:48.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:11:48.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:11:48.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:11:48.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:11:48.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:11:48.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:11:48.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:11:49.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:11:50.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:11:51.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:11:52.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:11:52.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:11:53.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:11:54.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:11:55.451 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:11:55.451 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 15:11:55.451 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 15:11:55.451 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:11:55.459 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.91 ms, Average inference time: 7.10 ms

2025-08-28 15:11:55.460 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:11:55.543 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:11:55.629 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch444
2025-08-28 15:11:58.442 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 444/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.138s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 3.187e-04, size: 480, ETA: 0:51:47
2025-08-28 15:12:01.422 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 444/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 3.181e-04, size: 448, ETA: 0:51:44
2025-08-28 15:12:04.564 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 444/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 1.0, lr: 3.175e-04, size: 320, ETA: 0:51:41
2025-08-28 15:12:07.694 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 444/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 3.169e-04, size: 384, ETA: 0:51:38
2025-08-28 15:12:10.738 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 444/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 3.163e-04, size: 480, ETA: 0:51:35
2025-08-28 15:12:13.783 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 444/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.8, lr: 3.157e-04, size: 448, ETA: 0:51:32
2025-08-28 15:12:15.195 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:12:21.323 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:12:21.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:12:22.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5811
2025-08-28 15:12:22.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4874
2025-08-28 15:12:22.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3923
2025-08-28 15:12:22.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4870
2025-08-28 15:12:22.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:12:22.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:12:22.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.581
2025-08-28 15:12:22.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-08-28 15:12:22.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-28 15:12:22.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-08-28 15:12:22.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:12:22.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:12:22.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:12:22.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:12:22.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:12:22.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:12:22.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:12:22.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:12:22.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:12:22.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:12:23.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:12:23.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:12:24.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:12:24.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:12:25.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:12:25.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:12:26.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:12:26.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:12:26.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:12:26.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:12:26.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:12:26.634 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.90 ms, Average inference time: 7.11 ms

2025-08-28 15:12:26.635 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:12:26.716 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:12:26.795 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch445
2025-08-28 15:12:29.741 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 445/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.7, lr: 3.149e-04, size: 448, ETA: 0:51:27
2025-08-28 15:12:32.734 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 445/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 1.4, cls_loss: 0.6, lr: 3.143e-04, size: 544, ETA: 0:51:24
2025-08-28 15:12:35.777 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 445/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 3.137e-04, size: 544, ETA: 0:51:21
2025-08-28 15:12:38.797 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 445/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.2, l1_loss: 1.5, conf_loss: 2.6, cls_loss: 0.8, lr: 3.131e-04, size: 544, ETA: 0:51:18
2025-08-28 15:12:41.797 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 445/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 3.125e-04, size: 480, ETA: 0:51:15
2025-08-28 15:12:44.942 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 445/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 3.119e-04, size: 512, ETA: 0:51:12
2025-08-28 15:12:46.363 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:12:52.581 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:12:53.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:12:53.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5720
2025-08-28 15:12:53.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5185
2025-08-28 15:12:53.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3837
2025-08-28 15:12:53.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4914
2025-08-28 15:12:53.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:12:53.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:12:53.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-08-28 15:12:53.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.518
2025-08-28 15:12:53.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-08-28 15:12:53.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.491
2025-08-28 15:12:53.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:12:53.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:12:53.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:12:53.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:12:53.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:12:53.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:12:53.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:12:53.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:12:53.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:12:54.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:12:54.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:12:55.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:12:55.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:12:56.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:12:56.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:12:57.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:12:57.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:12:58.470 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:12:58.470 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:12:58.470 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:12:58.470 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:12:58.477 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.91 ms, Average inference time: 7.15 ms

2025-08-28 15:12:58.478 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:12:58.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:12:58.642 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch446
2025-08-28 15:13:01.512 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 446/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 3.111e-04, size: 480, ETA: 0:51:07
2025-08-28 15:13:04.488 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 446/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 3.105e-04, size: 384, ETA: 0:51:04
2025-08-28 15:13:07.474 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 446/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.8, iou_loss: 1.9, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.8, lr: 3.099e-04, size: 544, ETA: 0:51:01
2025-08-28 15:13:10.497 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 446/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 3.093e-04, size: 256, ETA: 0:50:58
2025-08-28 15:13:13.499 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 446/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 3.087e-04, size: 352, ETA: 0:50:55
2025-08-28 15:13:16.570 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 446/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.8, lr: 3.081e-04, size: 416, ETA: 0:50:52
2025-08-28 15:13:17.904 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:13:24.156 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:13:24.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:13:25.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6019
2025-08-28 15:13:25.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5153
2025-08-28 15:13:25.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4162
2025-08-28 15:13:25.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5111
2025-08-28 15:13:25.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:13:25.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:13:25.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.602
2025-08-28 15:13:25.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 15:13:25.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-08-28 15:13:25.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-08-28 15:13:25.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:13:25.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:13:25.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:13:25.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:13:25.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:13:25.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:13:25.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:13:25.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:13:25.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:13:26.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:13:26.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:13:27.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:13:28.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:13:28.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:13:29.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:13:30.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:13:30.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:13:31.607 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:13:31.607 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:13:31.607 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:13:31.607 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:13:31.614 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.87 ms, Average inference time: 7.06 ms

2025-08-28 15:13:31.615 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:13:31.720 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:13:31.795 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch447
2025-08-28 15:13:34.857 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 447/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.8, lr: 3.073e-04, size: 256, ETA: 0:50:47
2025-08-28 15:13:37.887 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 447/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.8, lr: 3.067e-04, size: 320, ETA: 0:50:44
2025-08-28 15:13:40.802 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 447/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.7, lr: 3.061e-04, size: 416, ETA: 0:50:41
2025-08-28 15:13:43.722 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 447/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 3.055e-04, size: 416, ETA: 0:50:38
2025-08-28 15:13:46.864 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 447/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 3.049e-04, size: 576, ETA: 0:50:35
2025-08-28 15:13:49.868 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 447/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 3.043e-04, size: 384, ETA: 0:50:31
2025-08-28 15:13:51.209 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:13:57.302 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:13:57.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:13:58.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5588
2025-08-28 15:13:58.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4614
2025-08-28 15:13:58.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3804
2025-08-28 15:13:58.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4669
2025-08-28 15:13:58.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:13:58.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:13:58.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-08-28 15:13:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.461
2025-08-28 15:13:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-08-28 15:13:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.467
2025-08-28 15:13:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:13:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:13:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:13:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:13:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:13:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:13:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:13:58.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:13:58.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:13:58.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:13:59.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:13:59.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:14:00.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:14:00.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:14:01.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:14:01.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:14:02.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:14:02.690 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:14:02.690 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 15:14:02.691 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 15:14:02.691 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:14:02.701 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.06 ms, Average NMS time: 0.87 ms, Average inference time: 6.93 ms

2025-08-28 15:14:02.702 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:14:02.822 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:14:02.943 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch448
2025-08-28 15:14:05.867 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 448/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 3.035e-04, size: 256, ETA: 0:50:27
2025-08-28 15:14:08.940 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 448/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.6, lr: 3.029e-04, size: 256, ETA: 0:50:24
2025-08-28 15:14:11.965 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 448/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 3.023e-04, size: 288, ETA: 0:50:21
2025-08-28 15:14:14.889 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 448/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 3.018e-04, size: 256, ETA: 0:50:18
2025-08-28 15:14:18.064 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 448/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 15.2, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 15.2, cls_loss: 0.0, lr: 3.012e-04, size: 512, ETA: 0:50:15
2025-08-28 15:14:21.066 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 448/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 3.006e-04, size: 416, ETA: 0:50:11
2025-08-28 15:14:22.364 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:14:28.748 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:14:29.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:14:30.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5860
2025-08-28 15:14:30.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5034
2025-08-28 15:14:30.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3856
2025-08-28 15:14:30.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4916
2025-08-28 15:14:30.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:14:30.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:14:30.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-08-28 15:14:30.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-08-28 15:14:30.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.386
2025-08-28 15:14:30.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-08-28 15:14:30.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:14:30.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:14:30.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:14:30.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:14:30.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:14:30.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:14:30.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:14:30.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:14:30.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:14:31.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:14:32.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:14:32.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:14:33.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:14:34.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:14:35.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:14:36.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:14:36.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:14:37.650 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:14:37.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:14:37.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:14:37.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:14:37.658 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.93 ms, Average inference time: 7.18 ms

2025-08-28 15:14:37.659 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:14:37.740 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:14:37.819 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch449
2025-08-28 15:14:40.685 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 449/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.7, lr: 2.998e-04, size: 512, ETA: 0:50:07
2025-08-28 15:14:43.783 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 449/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.3, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.9, lr: 2.992e-04, size: 480, ETA: 0:50:04
2025-08-28 15:14:46.918 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 449/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 2.986e-04, size: 320, ETA: 0:50:01
2025-08-28 15:14:49.862 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 449/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 2.980e-04, size: 576, ETA: 0:49:58
2025-08-28 15:14:52.951 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 449/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.7, lr: 2.974e-04, size: 512, ETA: 0:49:55
2025-08-28 15:14:55.977 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 449/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.8, lr: 2.969e-04, size: 448, ETA: 0:49:51
2025-08-28 15:14:57.308 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:15:03.466 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:15:04.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:15:04.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5801
2025-08-28 15:15:04.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4943
2025-08-28 15:15:05.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4002
2025-08-28 15:15:05.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4916
2025-08-28 15:15:05.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:15:05.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:15:05.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.580
2025-08-28 15:15:05.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-08-28 15:15:05.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-28 15:15:05.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-08-28 15:15:05.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:15:05.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:15:05.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:15:05.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:15:05.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:15:05.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:15:05.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:15:05.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:15:05.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:15:05.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:15:06.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:15:07.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:15:07.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:15:08.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:15:09.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:15:09.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:15:10.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:15:11.306 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:15:11.307 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-08-28 15:15:11.307 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:15:11.307 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:15:11.314 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.10 ms, Average NMS time: 0.90 ms, Average inference time: 7.00 ms

2025-08-28 15:15:11.315 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:15:11.400 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:15:11.480 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch450
2025-08-28 15:15:14.507 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 450/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.7, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.7, lr: 2.960e-04, size: 256, ETA: 0:49:47
2025-08-28 15:15:17.424 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 450/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.5, lr: 2.955e-04, size: 512, ETA: 0:49:44
2025-08-28 15:15:20.472 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 450/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 2.949e-04, size: 256, ETA: 0:49:41
2025-08-28 15:15:23.402 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 450/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 2.943e-04, size: 576, ETA: 0:49:38
2025-08-28 15:15:26.535 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 450/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.9, lr: 2.937e-04, size: 288, ETA: 0:49:34
2025-08-28 15:15:29.706 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 450/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.006s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 2.932e-04, size: 256, ETA: 0:49:31
2025-08-28 15:15:31.061 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:15:37.264 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:15:38.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:15:39.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5903
2025-08-28 15:15:39.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4865
2025-08-28 15:15:39.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3931
2025-08-28 15:15:39.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4900
2025-08-28 15:15:39.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:15:39.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:15:39.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.590
2025-08-28 15:15:39.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-08-28 15:15:39.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-08-28 15:15:39.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-08-28 15:15:39.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:15:39.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:15:39.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:15:39.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:15:39.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:15:39.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:15:39.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:15:39.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:15:39.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:15:40.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:15:41.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:15:42.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:15:43.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:15:43.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:15:44.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:15:45.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:15:46.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:15:47.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:15:47.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:15:47.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:15:47.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:15:47.521 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.94 ms, Average inference time: 7.19 ms

2025-08-28 15:15:47.522 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:15:47.601 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:15:47.699 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch451
2025-08-28 15:15:50.552 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 451/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.7, lr: 2.923e-04, size: 512, ETA: 0:49:27
2025-08-28 15:15:53.654 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 451/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 2.917e-04, size: 576, ETA: 0:49:24
2025-08-28 15:15:56.626 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 451/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 2.912e-04, size: 256, ETA: 0:49:21
2025-08-28 15:15:59.688 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 451/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 2.906e-04, size: 512, ETA: 0:49:18
2025-08-28 15:16:02.682 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 451/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.7, lr: 2.900e-04, size: 512, ETA: 0:49:14
2025-08-28 15:16:05.753 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 451/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 2.895e-04, size: 448, ETA: 0:49:11
2025-08-28 15:16:07.120 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:16:13.444 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:16:14.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:16:15.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5833
2025-08-28 15:16:15.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5113
2025-08-28 15:16:15.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3801
2025-08-28 15:16:15.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4916
2025-08-28 15:16:15.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:16:15.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:16:15.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.583
2025-08-28 15:16:15.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-08-28 15:16:15.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-08-28 15:16:15.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-08-28 15:16:15.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:16:15.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:16:15.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:16:15.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:16:15.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:16:15.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:16:15.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:16:15.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:16:15.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:16:16.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:16:17.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:16:18.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:16:18.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:16:19.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:16:20.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:16:21.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:16:22.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:16:23.217 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:16:23.217 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:16:23.217 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:16:23.217 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:16:23.225 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.93 ms, Average inference time: 7.06 ms

2025-08-28 15:16:23.226 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:16:23.378 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:16:23.450 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch452
2025-08-28 15:16:26.463 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 452/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.8, lr: 2.886e-04, size: 448, ETA: 0:49:07
2025-08-28 15:16:29.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 452/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 4.1, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.6, lr: 2.881e-04, size: 288, ETA: 0:49:04
2025-08-28 15:16:32.529 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 452/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.3, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.8, lr: 2.875e-04, size: 576, ETA: 0:49:01
2025-08-28 15:16:35.477 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 452/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.7, lr: 2.869e-04, size: 288, ETA: 0:48:58
2025-08-28 15:16:38.473 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 452/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.4, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.5, lr: 2.864e-04, size: 512, ETA: 0:48:54
2025-08-28 15:16:41.559 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 452/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 2.858e-04, size: 320, ETA: 0:48:51
2025-08-28 15:16:42.906 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:16:49.085 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:16:50.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:16:50.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6012
2025-08-28 15:16:50.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4987
2025-08-28 15:16:50.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4295
2025-08-28 15:16:50.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5098
2025-08-28 15:16:50.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:16:50.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:16:50.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.601
2025-08-28 15:16:50.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-08-28 15:16:50.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.430
2025-08-28 15:16:50.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.510
2025-08-28 15:16:50.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:16:50.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:16:50.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:16:50.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:16:50.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:16:50.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:16:50.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:16:50.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:16:50.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:16:51.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:16:52.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:16:53.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:16:54.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:16:55.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:16:55.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:16:56.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:16:57.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:16:58.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:16:58.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:16:58.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:16:58.400 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:16:58.407 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.90 ms, Average inference time: 7.21 ms

2025-08-28 15:16:58.409 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:16:58.492 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:16:58.570 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch453
2025-08-28 15:17:01.423 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 453/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 1.0, lr: 2.850e-04, size: 384, ETA: 0:48:47
2025-08-28 15:17:04.493 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 453/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 2.844e-04, size: 320, ETA: 0:48:44
2025-08-28 15:17:07.658 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 453/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 2.838e-04, size: 256, ETA: 0:48:41
2025-08-28 15:17:10.742 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 453/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 2.833e-04, size: 576, ETA: 0:48:38
2025-08-28 15:17:13.734 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 453/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.7, lr: 2.827e-04, size: 416, ETA: 0:48:35
2025-08-28 15:17:16.899 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 453/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 2.821e-04, size: 544, ETA: 0:48:31
2025-08-28 15:17:18.250 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:17:24.453 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:17:25.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:17:26.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5865
2025-08-28 15:17:26.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4942
2025-08-28 15:17:26.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4010
2025-08-28 15:17:26.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4939
2025-08-28 15:17:26.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:17:26.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:17:26.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-08-28 15:17:26.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-08-28 15:17:26.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-08-28 15:17:26.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-08-28 15:17:26.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:17:26.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:17:26.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:17:26.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:17:26.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:17:26.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:17:26.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:17:26.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:17:26.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:17:27.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:17:28.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:17:28.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:17:29.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:17:30.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:17:31.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:17:32.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:17:33.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:17:33.970 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:17:33.970 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 15:17:33.970 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:17:33.970 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:17:33.977 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.93 ms, Average inference time: 7.12 ms

2025-08-28 15:17:33.978 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:17:34.059 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:17:34.141 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch454
2025-08-28 15:17:37.033 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 454/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 1.0, conf_loss: 1.2, cls_loss: 0.5, lr: 2.813e-04, size: 544, ETA: 0:48:27
2025-08-28 15:17:40.043 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 454/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 2.807e-04, size: 416, ETA: 0:48:24
2025-08-28 15:17:43.164 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 454/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 3.4, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.5, lr: 2.802e-04, size: 288, ETA: 0:48:21
2025-08-28 15:17:46.182 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 454/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.5, cls_loss: 0.6, lr: 2.796e-04, size: 288, ETA: 0:48:18
2025-08-28 15:17:49.270 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 454/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 2.791e-04, size: 448, ETA: 0:48:15
2025-08-28 15:17:52.384 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 454/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 2.785e-04, size: 544, ETA: 0:48:12
2025-08-28 15:17:53.684 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:17:59.897 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:18:00.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:18:01.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5939
2025-08-28 15:18:01.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5204
2025-08-28 15:18:01.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4148
2025-08-28 15:18:01.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5097
2025-08-28 15:18:01.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:18:01.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:18:01.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.594
2025-08-28 15:18:01.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-08-28 15:18:01.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-08-28 15:18:01.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.510
2025-08-28 15:18:01.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:18:01.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:18:01.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:18:01.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:18:01.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:18:01.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:18:01.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:18:01.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:18:01.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:18:02.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:18:03.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:18:03.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:18:04.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:18:05.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:18:06.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:18:06.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:18:07.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:18:08.492 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:18:08.492 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:18:08.492 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:18:08.492 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:18:08.499 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.91 ms, Average inference time: 7.16 ms

2025-08-28 15:18:08.500 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:18:08.577 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:18:08.656 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch455
2025-08-28 15:18:11.515 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 455/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 2.777e-04, size: 288, ETA: 0:48:07
2025-08-28 15:18:14.648 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 455/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 1.8, cls_loss: 0.7, lr: 2.771e-04, size: 576, ETA: 0:48:04
2025-08-28 15:18:17.709 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 455/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.8, lr: 2.766e-04, size: 480, ETA: 0:48:01
2025-08-28 15:18:20.726 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 455/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 2.760e-04, size: 384, ETA: 0:47:58
2025-08-28 15:18:23.771 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 455/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 2.754e-04, size: 544, ETA: 0:47:55
2025-08-28 15:18:26.916 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 455/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.5, lr: 2.749e-04, size: 352, ETA: 0:47:52
2025-08-28 15:18:28.279 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:18:34.422 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:18:35.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:18:35.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5612
2025-08-28 15:18:35.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5373
2025-08-28 15:18:35.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3818
2025-08-28 15:18:35.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4934
2025-08-28 15:18:35.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:18:35.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:18:35.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.561
2025-08-28 15:18:35.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-08-28 15:18:35.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-08-28 15:18:35.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-08-28 15:18:35.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:18:35.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:18:35.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:18:35.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:18:35.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:18:35.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:18:35.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:18:35.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:18:35.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:18:36.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:18:37.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:18:37.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:18:38.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:18:39.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:18:39.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:18:40.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:18:41.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:18:41.615 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:18:41.616 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 15:18:41.616 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:18:41.616 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:18:41.622 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.91 ms, Average inference time: 7.11 ms

2025-08-28 15:18:41.626 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:18:41.744 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:18:41.818 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch456
2025-08-28 15:18:44.637 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 456/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.138s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 2.741e-04, size: 320, ETA: 0:47:47
2025-08-28 15:18:47.573 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 456/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 2.735e-04, size: 288, ETA: 0:47:44
2025-08-28 15:18:50.516 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 456/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.5, lr: 2.730e-04, size: 448, ETA: 0:47:41
2025-08-28 15:18:53.490 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 456/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 2.724e-04, size: 416, ETA: 0:47:38
2025-08-28 15:18:56.483 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 456/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.7, lr: 2.718e-04, size: 352, ETA: 0:47:34
2025-08-28 15:18:59.472 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 456/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.004s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 2.713e-04, size: 448, ETA: 0:47:31
2025-08-28 15:19:00.891 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:19:07.168 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:19:07.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:19:08.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5868
2025-08-28 15:19:08.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5355
2025-08-28 15:19:08.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4002
2025-08-28 15:19:08.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5075
2025-08-28 15:19:08.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:19:08.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:19:08.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.587
2025-08-28 15:19:08.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-08-28 15:19:08.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-28 15:19:08.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.507
2025-08-28 15:19:08.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:19:08.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:19:08.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:19:08.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:19:08.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:19:08.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:19:08.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:19:08.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:19:08.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:19:09.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:19:09.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:19:10.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:19:11.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:19:11.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:19:12.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:19:12.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:19:13.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:19:14.144 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:19:14.144 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 15:19:14.144 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:19:14.144 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:19:14.152 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.95 ms, Average inference time: 7.21 ms

2025-08-28 15:19:14.152 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:19:14.231 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:19:14.311 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch457
2025-08-28 15:19:17.171 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 457/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 2.705e-04, size: 416, ETA: 0:47:27
2025-08-28 15:19:20.309 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 457/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 0.6, cls_loss: 0.5, lr: 2.699e-04, size: 384, ETA: 0:47:24
2025-08-28 15:19:23.256 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 457/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 2.694e-04, size: 352, ETA: 0:47:21
2025-08-28 15:19:26.315 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 457/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.8, lr: 2.688e-04, size: 256, ETA: 0:47:18
2025-08-28 15:19:29.372 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 457/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 2.683e-04, size: 544, ETA: 0:47:15
2025-08-28 15:19:32.467 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 457/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.8, lr: 2.677e-04, size: 256, ETA: 0:47:11
2025-08-28 15:19:33.848 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:19:40.191 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:19:41.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:19:41.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5765
2025-08-28 15:19:41.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4944
2025-08-28 15:19:41.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3682
2025-08-28 15:19:41.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4797
2025-08-28 15:19:41.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:19:41.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:19:41.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-08-28 15:19:41.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-08-28 15:19:41.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-28 15:19:41.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.480
2025-08-28 15:19:41.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:19:41.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:19:41.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:19:41.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:19:41.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:19:41.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:19:41.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:19:41.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:19:41.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:19:42.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:19:43.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:19:44.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:19:45.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:19:46.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:19:46.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:19:47.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:19:48.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:19:49.297 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:19:49.297 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:19:49.297 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 15:19:49.297 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:19:49.304 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.04 ms, Average NMS time: 0.93 ms, Average inference time: 6.97 ms

2025-08-28 15:19:49.305 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:19:49.385 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:19:49.467 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch458
2025-08-28 15:19:52.410 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 458/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.7, lr: 2.669e-04, size: 512, ETA: 0:47:07
2025-08-28 15:19:55.452 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 458/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 2.664e-04, size: 384, ETA: 0:47:04
2025-08-28 15:19:58.404 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 458/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.7, conf_loss: 2.1, cls_loss: 0.7, lr: 2.658e-04, size: 416, ETA: 0:47:01
2025-08-28 15:20:01.539 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 458/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.7, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.7, lr: 2.653e-04, size: 256, ETA: 0:46:58
2025-08-28 15:20:04.444 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 458/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 2.647e-04, size: 416, ETA: 0:46:55
2025-08-28 15:20:07.450 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 458/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 2.642e-04, size: 448, ETA: 0:46:51
2025-08-28 15:20:08.761 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:20:14.914 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:20:15.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:20:16.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5889
2025-08-28 15:20:16.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5251
2025-08-28 15:20:16.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3608
2025-08-28 15:20:16.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4916
2025-08-28 15:20:16.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:20:16.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:20:16.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-08-28 15:20:16.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 15:20:16.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-08-28 15:20:16.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-08-28 15:20:16.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:20:16.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:20:16.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:20:16.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:20:16.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:20:16.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:20:16.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:20:16.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:20:16.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:20:16.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:20:17.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:20:18.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:20:18.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:20:19.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:20:19.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:20:20.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:20:21.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:20:21.598 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:20:21.598 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:20:21.598 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:20:21.598 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:20:21.605 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.89 ms, Average inference time: 7.09 ms

2025-08-28 15:20:21.606 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:20:21.681 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:20:21.760 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch459
2025-08-28 15:20:24.588 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 459/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.138s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 1.2, lr: 2.634e-04, size: 448, ETA: 0:46:47
2025-08-28 15:20:27.538 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 459/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.6, lr: 2.628e-04, size: 384, ETA: 0:46:44
2025-08-28 15:20:30.575 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 459/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 2.623e-04, size: 512, ETA: 0:46:41
2025-08-28 15:20:33.689 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 459/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.7, lr: 2.617e-04, size: 576, ETA: 0:46:38
2025-08-28 15:20:36.994 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 459/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.6, lr: 2.612e-04, size: 256, ETA: 0:46:35
2025-08-28 15:20:40.105 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 459/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.006s, total_loss: 8.9, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 4.6, cls_loss: 0.7, lr: 2.606e-04, size: 448, ETA: 0:46:31
2025-08-28 15:20:41.443 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:20:47.533 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:20:48.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:20:48.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5740
2025-08-28 15:20:49.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5099
2025-08-28 15:20:49.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3824
2025-08-28 15:20:49.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4888
2025-08-28 15:20:49.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:20:49.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:20:49.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-08-28 15:20:49.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.510
2025-08-28 15:20:49.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-08-28 15:20:49.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.489
2025-08-28 15:20:49.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:20:49.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:20:49.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:20:49.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:20:49.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:20:49.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:20:49.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:20:49.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:20:49.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:20:49.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:20:50.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:20:51.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:20:51.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:20:52.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:20:53.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:20:54.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:20:54.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:20:55.472 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:20:55.472 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:20:55.472 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:20:55.472 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:20:55.480 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.93 ms, Average inference time: 7.10 ms

2025-08-28 15:20:55.481 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:20:55.643 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:20:55.715 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch460
2025-08-28 15:20:58.662 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 460/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.6, l1_loss: 2.1, conf_loss: 2.8, cls_loss: 1.0, lr: 2.598e-04, size: 576, ETA: 0:46:27
2025-08-28 15:21:02.035 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 460/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 2.593e-04, size: 352, ETA: 0:46:24
2025-08-28 15:21:05.012 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 460/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.4, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 0.9, lr: 2.587e-04, size: 320, ETA: 0:46:21
2025-08-28 15:21:08.026 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 460/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.6, lr: 2.582e-04, size: 384, ETA: 0:46:18
2025-08-28 15:21:11.026 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 460/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 2.576e-04, size: 416, ETA: 0:46:15
2025-08-28 15:21:13.944 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 460/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 2.571e-04, size: 544, ETA: 0:46:12
2025-08-28 15:21:15.366 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:21:21.464 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:21:22.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:21:22.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5792
2025-08-28 15:21:22.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5169
2025-08-28 15:21:22.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3728
2025-08-28 15:21:22.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4896
2025-08-28 15:21:22.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:21:22.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:21:22.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-08-28 15:21:22.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-08-28 15:21:22.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.373
2025-08-28 15:21:22.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-08-28 15:21:22.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:21:22.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:21:22.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:21:22.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:21:22.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:21:22.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:21:22.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:21:22.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:21:22.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:21:23.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:21:23.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:21:24.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:21:24.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:21:25.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:21:25.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:21:26.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:21:26.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:21:27.465 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:21:27.465 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:21:27.465 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:21:27.466 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:21:27.472 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.90 ms, Average inference time: 7.06 ms

2025-08-28 15:21:27.473 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:21:27.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:21:27.636 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch461
2025-08-28 15:21:30.378 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 461/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.136s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.7, cls_loss: 0.6, lr: 2.563e-04, size: 256, ETA: 0:46:07
2025-08-28 15:21:33.441 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 461/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 1.2, cls_loss: 0.7, lr: 2.558e-04, size: 384, ETA: 0:46:04
2025-08-28 15:21:36.556 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 461/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 2.552e-04, size: 576, ETA: 0:46:01
2025-08-28 15:21:39.716 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 461/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 2.547e-04, size: 448, ETA: 0:45:58
2025-08-28 15:21:42.798 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 461/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 2.541e-04, size: 352, ETA: 0:45:55
2025-08-28 15:21:45.876 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 461/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.005s, total_loss: 4.3, iou_loss: 1.5, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.5, lr: 2.536e-04, size: 576, ETA: 0:45:52
2025-08-28 15:21:47.264 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:21:53.422 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:21:54.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:21:55.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5844
2025-08-28 15:21:55.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4964
2025-08-28 15:21:55.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3655
2025-08-28 15:21:55.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4821
2025-08-28 15:21:55.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:21:55.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:21:55.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-08-28 15:21:55.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-08-28 15:21:55.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-08-28 15:21:55.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.482
2025-08-28 15:21:55.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:21:55.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:21:55.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:21:55.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:21:55.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:21:55.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:21:55.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:21:55.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:21:55.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:21:56.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:21:57.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:21:58.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:21:59.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:21:59.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:22:00.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:22:01.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:22:02.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:22:03.471 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:22:03.471 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:22:03.471 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 15:22:03.471 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:22:03.478 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.92 ms, Average inference time: 7.05 ms

2025-08-28 15:22:03.480 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:22:03.603 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:22:03.690 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch462
2025-08-28 15:22:06.557 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 462/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 2.528e-04, size: 544, ETA: 0:45:47
2025-08-28 15:22:09.525 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 462/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 2.523e-04, size: 448, ETA: 0:45:44
2025-08-28 15:22:12.603 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 462/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 2.517e-04, size: 384, ETA: 0:45:41
2025-08-28 15:22:15.632 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 462/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.7, lr: 2.512e-04, size: 320, ETA: 0:45:38
2025-08-28 15:22:18.963 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 462/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 2.507e-04, size: 576, ETA: 0:45:35
2025-08-28 15:22:22.178 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 462/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 2.501e-04, size: 448, ETA: 0:45:32
2025-08-28 15:22:23.551 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:22:29.785 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:22:30.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:22:31.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5920
2025-08-28 15:22:31.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5164
2025-08-28 15:22:31.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3995
2025-08-28 15:22:31.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5026
2025-08-28 15:22:31.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:22:31.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:22:31.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-08-28 15:22:31.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-08-28 15:22:31.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-08-28 15:22:31.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-08-28 15:22:31.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:22:31.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:22:31.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:22:31.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:22:31.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:22:31.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:22:31.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:22:31.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:22:31.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:22:32.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:22:33.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:22:34.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:22:35.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:22:36.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:22:37.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:22:38.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:22:39.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:22:40.028 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:22:40.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:22:40.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:22:40.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:22:40.036 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.92 ms, Average inference time: 7.15 ms

2025-08-28 15:22:40.037 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:22:40.115 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:22:40.194 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch463
2025-08-28 15:22:43.019 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 463/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.8, lr: 2.494e-04, size: 448, ETA: 0:45:27
2025-08-28 15:22:46.037 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 463/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 9.2, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 4.5, cls_loss: 0.7, lr: 2.488e-04, size: 576, ETA: 0:45:24
2025-08-28 15:22:49.104 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 463/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.4, l1_loss: 1.4, conf_loss: 2.9, cls_loss: 0.9, lr: 2.483e-04, size: 352, ETA: 0:45:21
2025-08-28 15:22:52.056 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 463/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 2.477e-04, size: 512, ETA: 0:45:18
2025-08-28 15:22:55.127 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 463/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.4, iou_loss: 1.7, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 2.472e-04, size: 576, ETA: 0:45:15
2025-08-28 15:22:58.124 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 463/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 2.467e-04, size: 416, ETA: 0:45:12
2025-08-28 15:22:59.439 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:23:05.613 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:23:06.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:23:07.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5878
2025-08-28 15:23:07.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5213
2025-08-28 15:23:07.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3772
2025-08-28 15:23:07.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4954
2025-08-28 15:23:07.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:23:07.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:23:07.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-08-28 15:23:07.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-08-28 15:23:07.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-08-28 15:23:07.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-08-28 15:23:07.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:23:07.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:23:07.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:23:07.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:23:07.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:23:07.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:23:07.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:23:07.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:23:07.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:23:08.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:23:09.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:23:10.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:23:10.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:23:11.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:23:12.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:23:13.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:23:14.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:23:15.151 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:23:15.151 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:23:15.151 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:23:15.151 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:23:15.159 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.97 ms, Average inference time: 7.13 ms

2025-08-28 15:23:15.160 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:23:15.291 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:23:15.365 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch464
2025-08-28 15:23:18.270 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 464/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.8, lr: 2.459e-04, size: 352, ETA: 0:45:07
2025-08-28 15:23:21.297 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 464/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 2.454e-04, size: 320, ETA: 0:45:04
2025-08-28 15:23:24.214 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 464/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 2.448e-04, size: 352, ETA: 0:45:01
2025-08-28 15:23:27.209 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 464/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 2.443e-04, size: 480, ETA: 0:44:58
2025-08-28 15:23:30.266 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 464/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 2.438e-04, size: 416, ETA: 0:44:55
2025-08-28 15:23:33.233 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 464/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 2.432e-04, size: 256, ETA: 0:44:52
2025-08-28 15:23:34.621 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:23:40.896 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:23:41.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:23:42.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5878
2025-08-28 15:23:42.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4934
2025-08-28 15:23:42.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3904
2025-08-28 15:23:42.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4905
2025-08-28 15:23:42.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:23:42.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:23:42.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-08-28 15:23:42.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-08-28 15:23:42.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-28 15:23:42.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.491
2025-08-28 15:23:42.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:23:42.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:23:42.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:23:42.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:23:42.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:23:42.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:23:42.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:23:42.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:23:42.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:23:43.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:23:43.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:23:44.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:23:45.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:23:45.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:23:46.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:23:47.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:23:47.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:23:48.395 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:23:48.395 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:23:48.396 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:23:48.396 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:23:48.403 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.93 ms, Average inference time: 7.26 ms

2025-08-28 15:23:48.404 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:23:48.485 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:23:48.563 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch465
2025-08-28 15:23:51.532 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 465/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 2.425e-04, size: 544, ETA: 0:44:47
2025-08-28 15:23:54.566 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 465/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.6, lr: 2.419e-04, size: 320, ETA: 0:44:44
2025-08-28 15:23:57.574 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 465/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 2.414e-04, size: 544, ETA: 0:44:41
2025-08-28 15:24:00.582 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 465/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 2.409e-04, size: 512, ETA: 0:44:38
2025-08-28 15:24:03.593 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 465/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.8, lr: 2.404e-04, size: 512, ETA: 0:44:35
2025-08-28 15:24:06.709 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 465/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 7.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 3.7, cls_loss: 1.0, lr: 2.398e-04, size: 320, ETA: 0:44:32
2025-08-28 15:24:08.072 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:24:14.240 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:24:15.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:24:15.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5932
2025-08-28 15:24:15.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5055
2025-08-28 15:24:15.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3901
2025-08-28 15:24:15.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4963
2025-08-28 15:24:15.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:24:15.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:24:15.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-08-28 15:24:15.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-08-28 15:24:15.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-28 15:24:15.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.496
2025-08-28 15:24:15.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:24:15.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:24:15.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:24:15.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:24:15.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:24:15.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:24:15.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:24:15.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:24:15.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:24:16.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:24:17.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:24:18.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:24:18.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:24:19.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:24:20.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:24:20.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:24:21.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:24:22.311 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:24:22.312 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:24:22.312 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:24:22.312 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:24:22.320 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.94 ms, Average inference time: 7.13 ms

2025-08-28 15:24:22.320 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:24:22.463 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:24:22.538 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch466
2025-08-28 15:24:25.307 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 466/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.136s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 2.391e-04, size: 256, ETA: 0:44:27
2025-08-28 15:24:28.496 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 466/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 2.385e-04, size: 352, ETA: 0:44:24
2025-08-28 15:24:31.682 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 466/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 2.380e-04, size: 288, ETA: 0:44:21
2025-08-28 15:24:34.698 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 466/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.8, iou_loss: 3.7, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.7, lr: 2.375e-04, size: 448, ETA: 0:44:18
2025-08-28 15:24:37.844 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 466/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 2.370e-04, size: 352, ETA: 0:44:15
2025-08-28 15:24:40.896 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 466/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.4, l1_loss: 0.6, conf_loss: 2.9, cls_loss: 0.6, lr: 2.364e-04, size: 544, ETA: 0:44:12
2025-08-28 15:24:42.304 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:24:48.625 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:24:49.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:24:49.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5902
2025-08-28 15:24:49.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5212
2025-08-28 15:24:49.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3906
2025-08-28 15:24:49.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5007
2025-08-28 15:24:49.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:24:49.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:24:49.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.590
2025-08-28 15:24:49.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-08-28 15:24:49.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-08-28 15:24:49.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-08-28 15:24:49.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:24:49.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:24:49.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:24:49.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:24:49.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:24:49.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:24:49.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:24:49.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:24:49.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:24:50.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:24:51.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:24:51.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:24:52.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:24:52.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:24:53.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:24:54.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:24:54.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:24:55.179 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:24:55.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:24:55.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:24:55.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:24:55.187 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.92 ms, Average inference time: 7.21 ms

2025-08-28 15:24:55.188 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:24:55.272 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:24:55.365 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch467
2025-08-28 15:24:58.343 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 467/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.5, lr: 2.357e-04, size: 384, ETA: 0:44:07
2025-08-28 15:25:01.407 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 467/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 2.352e-04, size: 416, ETA: 0:44:04
2025-08-28 15:25:04.546 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 467/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.7, lr: 2.346e-04, size: 448, ETA: 0:44:01
2025-08-28 15:25:07.598 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 467/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 2.341e-04, size: 448, ETA: 0:43:58
2025-08-28 15:25:10.702 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 467/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.9, lr: 2.336e-04, size: 480, ETA: 0:43:55
2025-08-28 15:25:13.717 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 467/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 8.0, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.8, lr: 2.331e-04, size: 384, ETA: 0:43:52
2025-08-28 15:25:15.121 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:25:21.239 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:25:21.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:25:22.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5865
2025-08-28 15:25:22.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5075
2025-08-28 15:25:22.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3555
2025-08-28 15:25:22.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4832
2025-08-28 15:25:22.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:25:22.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:25:22.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.587
2025-08-28 15:25:22.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-08-28 15:25:22.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-08-28 15:25:22.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.483
2025-08-28 15:25:22.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:25:22.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:25:22.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:25:22.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:25:22.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:25:22.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:25:22.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:25:22.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:25:22.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:25:23.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:25:23.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:25:24.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:25:25.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:25:25.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:25:26.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:25:26.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:25:27.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:25:28.025 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:25:28.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 15:25:28.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 15:25:28.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:25:28.033 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.90 ms, Average inference time: 7.02 ms

2025-08-28 15:25:28.034 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:25:28.123 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:25:28.202 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch468
2025-08-28 15:25:31.138 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 468/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 2.323e-04, size: 512, ETA: 0:43:47
2025-08-28 15:25:34.192 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 468/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 2.318e-04, size: 352, ETA: 0:43:44
2025-08-28 15:25:37.176 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 468/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 1.0, lr: 2.313e-04, size: 448, ETA: 0:43:41
2025-08-28 15:25:40.403 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 468/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.2, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 2.3, cls_loss: 0.8, lr: 2.308e-04, size: 288, ETA: 0:43:38
2025-08-28 15:25:43.419 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 468/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 2.302e-04, size: 544, ETA: 0:43:35
2025-08-28 15:25:46.554 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 468/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.9, lr: 2.297e-04, size: 480, ETA: 0:43:32
2025-08-28 15:25:47.931 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:25:54.113 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:25:54.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:25:55.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5986
2025-08-28 15:25:55.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5115
2025-08-28 15:25:55.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3904
2025-08-28 15:25:55.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5001
2025-08-28 15:25:55.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:25:55.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:25:55.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-08-28 15:25:55.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-08-28 15:25:55.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-28 15:25:55.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-08-28 15:25:55.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:25:55.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:25:55.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:25:55.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:25:55.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:25:55.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:25:55.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:25:55.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:25:55.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:25:56.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:25:57.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:25:57.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:25:58.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:25:59.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:25:59.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:26:00.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:26:01.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:26:01.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:26:01.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:26:01.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:26:01.887 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:26:01.893 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.92 ms, Average inference time: 7.05 ms

2025-08-28 15:26:01.895 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:26:01.971 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:26:02.050 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch469
2025-08-28 15:26:04.838 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 469/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.136s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 1.1, lr: 2.290e-04, size: 256, ETA: 0:43:28
2025-08-28 15:26:07.792 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 469/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 2.285e-04, size: 576, ETA: 0:43:24
2025-08-28 15:26:10.942 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 469/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.5, lr: 2.279e-04, size: 480, ETA: 0:43:21
2025-08-28 15:26:14.104 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 469/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 2.274e-04, size: 256, ETA: 0:43:18
2025-08-28 15:26:17.120 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 469/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 4.1, cls_loss: 0.7, lr: 2.269e-04, size: 480, ETA: 0:43:15
2025-08-28 15:26:20.178 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 469/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.3, cls_loss: 0.7, lr: 2.264e-04, size: 288, ETA: 0:43:12
2025-08-28 15:26:21.556 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:26:27.884 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:26:28.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:26:29.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5876
2025-08-28 15:26:29.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5104
2025-08-28 15:26:29.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4180
2025-08-28 15:26:29.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5053
2025-08-28 15:26:29.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:26:29.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:26:29.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-08-28 15:26:29.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.510
2025-08-28 15:26:29.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.418
2025-08-28 15:26:29.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-08-28 15:26:29.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:26:29.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:26:29.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:26:29.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:26:29.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:26:29.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:26:29.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:26:29.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:26:29.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:26:30.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:26:31.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:26:32.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:26:33.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:26:34.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:26:35.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:26:36.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:26:37.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:26:38.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:26:38.047 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:26:38.047 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:26:38.047 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:26:38.054 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.94 ms, Average inference time: 7.13 ms

2025-08-28 15:26:38.055 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:26:38.134 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:26:38.217 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch470
2025-08-28 15:26:41.090 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 470/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.6, lr: 2.256e-04, size: 544, ETA: 0:43:08
2025-08-28 15:26:44.105 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 470/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 2.251e-04, size: 448, ETA: 0:43:05
2025-08-28 15:26:47.154 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 470/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 2.246e-04, size: 256, ETA: 0:43:01
2025-08-28 15:26:50.143 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 470/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.7, cls_loss: 0.6, lr: 2.241e-04, size: 288, ETA: 0:42:58
2025-08-28 15:26:53.197 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 470/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.8, lr: 2.236e-04, size: 352, ETA: 0:42:55
2025-08-28 15:26:56.230 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 470/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 2.231e-04, size: 288, ETA: 0:42:52
2025-08-28 15:26:57.537 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:27:03.731 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:27:04.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:27:05.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6016
2025-08-28 15:27:05.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5030
2025-08-28 15:27:05.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3868
2025-08-28 15:27:05.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4971
2025-08-28 15:27:05.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:27:05.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:27:05.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.602
2025-08-28 15:27:05.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-08-28 15:27:05.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-28 15:27:05.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-08-28 15:27:05.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:27:05.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:27:05.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:27:05.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:27:05.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:27:05.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:27:05.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:27:05.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:27:05.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:27:06.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:27:07.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:27:08.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:27:09.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:27:09.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:27:10.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:27:11.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:27:12.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:27:13.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:27:13.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:27:13.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:27:13.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:27:13.598 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.93 ms, Average inference time: 7.07 ms

2025-08-28 15:27:13.599 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:27:13.683 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:27:13.762 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch471
2025-08-28 15:27:16.747 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 471/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 2.223e-04, size: 448, ETA: 0:42:48
2025-08-28 15:27:19.699 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 471/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.8, lr: 2.218e-04, size: 352, ETA: 0:42:45
2025-08-28 15:27:22.741 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 471/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 1.0, lr: 2.213e-04, size: 384, ETA: 0:42:41
2025-08-28 15:27:25.667 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 471/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 2.208e-04, size: 320, ETA: 0:42:38
2025-08-28 15:27:28.818 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 471/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 2.203e-04, size: 480, ETA: 0:42:35
2025-08-28 15:27:31.847 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 471/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.7, lr: 2.198e-04, size: 480, ETA: 0:42:32
2025-08-28 15:27:33.291 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:27:39.415 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:27:40.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:27:40.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5668
2025-08-28 15:27:40.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4824
2025-08-28 15:27:40.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3471
2025-08-28 15:27:40.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4654
2025-08-28 15:27:40.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:27:40.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:27:40.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.567
2025-08-28 15:27:40.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-08-28 15:27:40.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-08-28 15:27:40.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.465
2025-08-28 15:27:40.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:27:40.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:27:40.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:27:40.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:27:40.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:27:40.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:27:40.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:27:40.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:27:40.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:27:41.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:27:41.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:27:42.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:27:42.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:27:43.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:27:43.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:27:44.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:27:44.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:27:45.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:27:45.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 15:27:45.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 15:27:45.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:27:45.228 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.90 ms, Average inference time: 7.06 ms

2025-08-28 15:27:45.230 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:27:45.310 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:27:45.431 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch472
2025-08-28 15:27:48.316 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 472/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 1.3, lr: 2.191e-04, size: 352, ETA: 0:42:28
2025-08-28 15:27:51.349 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 472/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 2.186e-04, size: 256, ETA: 0:42:25
2025-08-28 15:27:54.395 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 472/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 2.180e-04, size: 288, ETA: 0:42:22
2025-08-28 15:27:57.440 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 472/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 1.0, lr: 2.175e-04, size: 384, ETA: 0:42:18
2025-08-28 15:28:00.618 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 472/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.8, lr: 2.170e-04, size: 384, ETA: 0:42:15
2025-08-28 15:28:03.595 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 472/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.8, lr: 2.165e-04, size: 288, ETA: 0:42:12
2025-08-28 15:28:04.999 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:28:11.273 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:28:12.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:28:12.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6049
2025-08-28 15:28:12.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5236
2025-08-28 15:28:12.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4141
2025-08-28 15:28:12.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5142
2025-08-28 15:28:12.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:28:12.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:28:12.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.605
2025-08-28 15:28:12.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-08-28 15:28:12.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-08-28 15:28:12.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-08-28 15:28:12.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:28:12.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:28:12.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:28:12.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:28:12.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:28:12.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:28:12.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:28:12.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:28:12.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:28:13.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:28:14.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:28:14.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:28:15.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:28:16.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:28:16.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:28:17.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:28:18.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:28:19.062 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:28:19.062 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:28:19.062 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:28:19.063 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:28:19.069 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.92 ms, Average inference time: 7.24 ms

2025-08-28 15:28:19.070 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:28:19.146 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:28:19.225 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch473
2025-08-28 15:28:22.105 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 473/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 4.2, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 2.158e-04, size: 384, ETA: 0:42:08
2025-08-28 15:28:25.238 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 473/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 2.153e-04, size: 288, ETA: 0:42:05
2025-08-28 15:28:28.284 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 473/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.4, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 2.148e-04, size: 544, ETA: 0:42:02
2025-08-28 15:28:31.216 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 473/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 1.1, lr: 2.143e-04, size: 352, ETA: 0:41:59
2025-08-28 15:28:34.330 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 473/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.7, lr: 2.138e-04, size: 352, ETA: 0:41:55
2025-08-28 15:28:37.532 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 473/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 2.7, cls_loss: 0.7, lr: 2.133e-04, size: 576, ETA: 0:41:52
2025-08-28 15:28:38.889 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:28:45.172 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:28:45.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:28:46.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5768
2025-08-28 15:28:46.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4983
2025-08-28 15:28:46.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3897
2025-08-28 15:28:46.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4882
2025-08-28 15:28:46.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:28:46.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:28:46.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.577
2025-08-28 15:28:46.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-08-28 15:28:46.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-28 15:28:46.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-08-28 15:28:46.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:28:46.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:28:46.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:28:46.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:28:46.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:28:46.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:28:46.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:28:46.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:28:46.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:28:47.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:28:47.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:28:48.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:28:48.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:28:49.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:28:49.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:28:50.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:28:50.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:28:51.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:28:51.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:28:51.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:28:51.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:28:51.381 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.39 ms, Average NMS time: 0.90 ms, Average inference time: 7.29 ms

2025-08-28 15:28:51.382 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:28:51.462 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:28:51.543 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch474
2025-08-28 15:28:54.472 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 474/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 8.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 4.5, cls_loss: 0.9, lr: 2.126e-04, size: 544, ETA: 0:41:48
2025-08-28 15:28:57.533 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 474/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.6, lr: 2.121e-04, size: 544, ETA: 0:41:45
2025-08-28 15:29:00.461 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 474/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 2.116e-04, size: 384, ETA: 0:41:42
2025-08-28 15:29:03.608 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 474/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 2.111e-04, size: 288, ETA: 0:41:39
2025-08-28 15:29:06.781 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 474/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 6.2, iou_loss: 1.9, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.6, lr: 2.106e-04, size: 576, ETA: 0:41:36
2025-08-28 15:29:09.911 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 474/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 2.101e-04, size: 576, ETA: 0:41:33
2025-08-28 15:29:11.380 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:29:17.441 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:29:18.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:29:18.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5928
2025-08-28 15:29:18.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5125
2025-08-28 15:29:18.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3987
2025-08-28 15:29:18.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5013
2025-08-28 15:29:18.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:29:18.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:29:18.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-08-28 15:29:18.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-08-28 15:29:18.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-08-28 15:29:18.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-08-28 15:29:18.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:29:18.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:29:18.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:29:18.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:29:18.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:29:18.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:29:18.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:29:18.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:29:18.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:29:19.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:29:20.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:29:20.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:29:21.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:29:21.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:29:22.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:29:22.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:29:23.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:29:24.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:29:24.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:29:24.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:29:24.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:29:24.119 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.91 ms, Average inference time: 7.05 ms

2025-08-28 15:29:24.120 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:29:24.204 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:29:24.283 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch475
2025-08-28 15:29:27.180 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 475/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 2.093e-04, size: 448, ETA: 0:41:28
2025-08-28 15:29:30.346 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 475/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 2.089e-04, size: 352, ETA: 0:41:25
2025-08-28 15:29:33.338 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 475/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 2.084e-04, size: 416, ETA: 0:41:22
2025-08-28 15:29:36.385 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 475/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 2.079e-04, size: 256, ETA: 0:41:19
2025-08-28 15:29:39.530 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 475/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 2.074e-04, size: 384, ETA: 0:41:16
2025-08-28 15:29:42.662 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 475/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.7, lr: 2.069e-04, size: 256, ETA: 0:41:13
2025-08-28 15:29:44.017 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:29:50.184 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:29:50.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:29:51.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5981
2025-08-28 15:29:51.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5298
2025-08-28 15:29:51.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4143
2025-08-28 15:29:51.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5141
2025-08-28 15:29:51.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:29:51.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:29:51.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-08-28 15:29:51.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 15:29:51.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-08-28 15:29:51.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-08-28 15:29:51.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:29:51.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:29:51.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:29:51.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:29:51.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:29:51.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:29:51.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:29:51.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:29:51.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:29:52.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:29:53.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:29:53.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:29:54.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:29:55.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:29:55.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:29:56.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:29:57.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:29:57.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:29:57.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 15:29:57.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:29:57.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:29:57.960 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.89 ms, Average inference time: 7.05 ms

2025-08-28 15:29:57.961 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:29:58.046 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:29:58.129 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch476
2025-08-28 15:30:01.111 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 476/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.5, lr: 2.062e-04, size: 352, ETA: 0:41:08
2025-08-28 15:30:04.152 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 476/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 2.057e-04, size: 544, ETA: 0:41:05
2025-08-28 15:30:07.166 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 476/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.8, lr: 2.052e-04, size: 448, ETA: 0:41:02
2025-08-28 15:30:10.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 476/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.9, lr: 2.047e-04, size: 480, ETA: 0:40:59
2025-08-28 15:30:13.344 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 476/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.7, lr: 2.042e-04, size: 480, ETA: 0:40:56
2025-08-28 15:30:16.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 476/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 2.037e-04, size: 320, ETA: 0:40:53
2025-08-28 15:30:17.764 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:30:23.960 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:30:24.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:30:25.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5998
2025-08-28 15:30:25.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5257
2025-08-28 15:30:25.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3995
2025-08-28 15:30:25.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5083
2025-08-28 15:30:25.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:30:25.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:30:25.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-08-28 15:30:25.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-08-28 15:30:25.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-08-28 15:30:25.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.508
2025-08-28 15:30:25.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:30:25.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:30:25.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:30:25.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:30:25.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:30:25.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:30:25.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:30:25.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:30:25.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:30:26.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:30:27.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:30:28.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:30:29.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:30:29.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:30:30.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:30:31.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:30:32.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:30:33.285 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:30:33.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:30:33.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:30:33.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:30:33.293 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.92 ms, Average inference time: 7.12 ms

2025-08-28 15:30:33.294 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:30:33.372 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:30:33.454 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch477
2025-08-28 15:30:36.317 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 477/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 1.0, lr: 2.030e-04, size: 320, ETA: 0:40:48
2025-08-28 15:30:39.272 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 477/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 2.025e-04, size: 416, ETA: 0:40:45
2025-08-28 15:30:42.311 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 477/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 2.020e-04, size: 256, ETA: 0:40:42
2025-08-28 15:30:45.329 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 477/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 2.015e-04, size: 320, ETA: 0:40:39
2025-08-28 15:30:48.380 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 477/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 2.010e-04, size: 352, ETA: 0:40:36
2025-08-28 15:30:51.301 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 477/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 2.005e-04, size: 480, ETA: 0:40:33
2025-08-28 15:30:52.628 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:30:58.843 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:30:59.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:30:59.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5811
2025-08-28 15:31:00.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5154
2025-08-28 15:31:00.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3708
2025-08-28 15:31:00.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4891
2025-08-28 15:31:00.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:31:00.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:31:00.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.581
2025-08-28 15:31:00.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 15:31:00.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-08-28 15:31:00.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.489
2025-08-28 15:31:00.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:31:00.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:31:00.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:31:00.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:31:00.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:31:00.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:31:00.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:31:00.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:31:00.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:31:00.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:31:01.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:31:01.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:31:02.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:31:03.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:31:03.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:31:04.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:31:04.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:31:05.329 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:31:05.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:31:05.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:31:05.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:31:05.337 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.92 ms, Average inference time: 7.12 ms

2025-08-28 15:31:05.338 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:31:05.461 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:31:05.534 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch478
2025-08-28 15:31:08.496 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 478/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 1.998e-04, size: 416, ETA: 0:40:28
2025-08-28 15:31:11.459 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 478/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 1.0, lr: 1.993e-04, size: 320, ETA: 0:40:25
2025-08-28 15:31:14.712 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 478/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 1.989e-04, size: 352, ETA: 0:40:22
2025-08-28 15:31:17.928 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 478/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.984e-04, size: 256, ETA: 0:40:19
2025-08-28 15:31:20.923 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 478/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.5, lr: 1.979e-04, size: 576, ETA: 0:40:16
2025-08-28 15:31:24.020 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 478/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 1.974e-04, size: 480, ETA: 0:40:13
2025-08-28 15:31:25.385 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:31:31.789 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:31:32.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:31:33.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5826
2025-08-28 15:31:33.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5100
2025-08-28 15:31:33.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3717
2025-08-28 15:31:33.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4881
2025-08-28 15:31:33.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:31:33.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:31:33.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.583
2025-08-28 15:31:33.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.510
2025-08-28 15:31:33.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-08-28 15:31:33.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-08-28 15:31:33.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:31:33.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:31:33.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:31:33.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:31:33.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:31:33.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:31:33.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:31:33.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:31:33.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:31:33.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:31:34.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:31:35.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:31:35.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:31:36.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:31:37.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:31:37.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:31:38.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:31:39.091 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:31:39.092 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:31:39.092 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:31:39.092 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:31:39.099 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.90 ms, Average inference time: 7.06 ms

2025-08-28 15:31:39.100 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:31:39.181 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:31:39.260 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch479
2025-08-28 15:31:42.103 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 479/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.967e-04, size: 288, ETA: 0:40:08
2025-08-28 15:31:45.061 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 479/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.6, lr: 1.962e-04, size: 416, ETA: 0:40:05
2025-08-28 15:31:48.044 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 479/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 1.957e-04, size: 320, ETA: 0:40:02
2025-08-28 15:31:51.255 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 479/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 1.953e-04, size: 384, ETA: 0:39:59
2025-08-28 15:31:54.344 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 479/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.6, lr: 1.948e-04, size: 544, ETA: 0:39:56
2025-08-28 15:31:57.346 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 479/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.7, lr: 1.943e-04, size: 288, ETA: 0:39:53
2025-08-28 15:31:58.754 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:32:04.840 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:32:05.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:32:06.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5905
2025-08-28 15:32:06.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5126
2025-08-28 15:32:06.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3752
2025-08-28 15:32:06.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4928
2025-08-28 15:32:06.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:32:06.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:32:06.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.591
2025-08-28 15:32:06.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-08-28 15:32:06.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-08-28 15:32:06.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-08-28 15:32:06.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:32:06.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:32:06.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:32:06.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:32:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:32:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:32:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:32:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:32:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:32:07.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:32:08.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:32:08.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:32:09.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:32:10.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:32:10.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:32:11.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:32:12.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:32:13.275 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:32:13.275 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:32:13.275 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:32:13.276 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:32:13.283 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.93 ms, Average inference time: 7.07 ms

2025-08-28 15:32:13.284 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:32:13.368 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:32:13.451 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch480
2025-08-28 15:32:16.313 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 480/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.7, lr: 1.936e-04, size: 512, ETA: 0:39:49
2025-08-28 15:32:19.299 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 480/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.8, lr: 1.931e-04, size: 320, ETA: 0:39:45
2025-08-28 15:32:22.579 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 480/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 1.5, lr: 1.926e-04, size: 480, ETA: 0:39:42
2025-08-28 15:32:25.729 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 480/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.922e-04, size: 320, ETA: 0:39:39
2025-08-28 15:32:28.938 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 480/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.8, lr: 1.917e-04, size: 320, ETA: 0:39:36
2025-08-28 15:32:31.947 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 480/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.912e-04, size: 320, ETA: 0:39:33
2025-08-28 15:32:33.281 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:32:39.410 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:32:40.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:32:40.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5957
2025-08-28 15:32:41.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5208
2025-08-28 15:32:41.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4058
2025-08-28 15:32:41.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5074
2025-08-28 15:32:41.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:32:41.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:32:41.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.596
2025-08-28 15:32:41.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-08-28 15:32:41.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-08-28 15:32:41.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.507
2025-08-28 15:32:41.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:32:41.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:32:41.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:32:41.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:32:41.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:32:41.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:32:41.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:32:41.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:32:41.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:32:41.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:32:42.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:32:43.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:32:44.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:32:45.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:32:45.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:32:46.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:32:47.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:32:48.144 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:32:48.144 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:32:48.144 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:32:48.144 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:32:48.151 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.91 ms, Average inference time: 7.03 ms

2025-08-28 15:32:48.152 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:32:48.234 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:32:48.314 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch481
2025-08-28 15:32:51.180 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 481/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.8, lr: 1.905e-04, size: 448, ETA: 0:39:29
2025-08-28 15:32:54.166 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 481/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.5, lr: 1.900e-04, size: 480, ETA: 0:39:26
2025-08-28 15:32:57.250 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 481/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.6, lr: 1.896e-04, size: 576, ETA: 0:39:23
2025-08-28 15:33:00.372 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 481/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 1.891e-04, size: 352, ETA: 0:39:20
2025-08-28 15:33:03.411 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 481/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.8, lr: 1.886e-04, size: 288, ETA: 0:39:16
2025-08-28 15:33:06.474 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 481/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 1.881e-04, size: 384, ETA: 0:39:13
2025-08-28 15:33:07.944 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:33:14.257 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:33:14.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:33:15.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5778
2025-08-28 15:33:15.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5167
2025-08-28 15:33:15.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4117
2025-08-28 15:33:15.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5021
2025-08-28 15:33:15.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:33:15.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:33:15.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-08-28 15:33:15.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-08-28 15:33:15.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-08-28 15:33:15.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-08-28 15:33:15.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:33:15.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:33:15.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:33:15.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:33:15.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:33:15.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:33:15.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:33:15.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:33:15.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:33:16.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:33:16.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:33:17.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:33:17.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:33:18.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:33:18.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:33:19.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:33:19.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:33:20.469 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:33:20.469 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:33:20.469 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:33:20.470 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:33:20.477 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.91 ms, Average inference time: 7.09 ms

2025-08-28 15:33:20.478 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:33:20.560 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:33:20.684 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch482
2025-08-28 15:33:23.536 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 482/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 1.874e-04, size: 384, ETA: 0:39:09
2025-08-28 15:33:26.858 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 482/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.870e-04, size: 288, ETA: 0:39:06
2025-08-28 15:33:29.878 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 482/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.8, lr: 1.865e-04, size: 480, ETA: 0:39:03
2025-08-28 15:33:32.979 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 482/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 1.860e-04, size: 384, ETA: 0:39:00
2025-08-28 15:33:35.918 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 482/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.003s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 1.856e-04, size: 256, ETA: 0:38:57
2025-08-28 15:33:38.983 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 482/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 10.7, iou_loss: 3.3, l1_loss: 1.0, conf_loss: 5.0, cls_loss: 1.3, lr: 1.851e-04, size: 512, ETA: 0:38:54
2025-08-28 15:33:40.394 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:33:46.488 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:33:47.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:33:47.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5982
2025-08-28 15:33:47.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5368
2025-08-28 15:33:47.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3867
2025-08-28 15:33:47.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5072
2025-08-28 15:33:47.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:33:47.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:33:47.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-08-28 15:33:47.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-08-28 15:33:47.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-28 15:33:47.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.507
2025-08-28 15:33:47.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:33:47.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:33:47.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:33:47.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:33:47.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:33:47.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:33:47.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:33:47.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:33:47.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:33:48.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:33:48.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:33:49.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:33:50.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:33:50.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:33:51.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:33:51.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:33:52.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:33:53.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:33:53.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:33:53.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:33:53.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:33:53.038 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.91 ms, Average inference time: 7.12 ms

2025-08-28 15:33:53.039 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:33:53.168 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:33:53.240 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch483
2025-08-28 15:33:56.126 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 483/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 1.844e-04, size: 384, ETA: 0:38:49
2025-08-28 15:33:59.100 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 483/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 1.839e-04, size: 480, ETA: 0:38:46
2025-08-28 15:34:02.103 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 483/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 1.835e-04, size: 384, ETA: 0:38:43
2025-08-28 15:34:05.198 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 483/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 10.7, iou_loss: 3.7, l1_loss: 1.4, conf_loss: 4.9, cls_loss: 0.7, lr: 1.830e-04, size: 576, ETA: 0:38:40
2025-08-28 15:34:08.310 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 483/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 1.825e-04, size: 576, ETA: 0:38:37
2025-08-28 15:34:11.317 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 483/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.9, lr: 1.821e-04, size: 320, ETA: 0:38:34
2025-08-28 15:34:12.632 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:34:18.793 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:34:19.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:34:20.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5908
2025-08-28 15:34:20.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5120
2025-08-28 15:34:20.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3938
2025-08-28 15:34:20.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4989
2025-08-28 15:34:20.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:34:20.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:34:20.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.591
2025-08-28 15:34:20.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-08-28 15:34:20.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-08-28 15:34:20.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.499
2025-08-28 15:34:20.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:34:20.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:34:20.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:34:20.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:34:20.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:34:20.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:34:20.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:34:20.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:34:20.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:34:21.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:34:22.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:34:23.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:34:24.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:34:25.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:34:26.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:34:27.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:34:28.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:34:29.198 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:34:29.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:34:29.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:34:29.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:34:29.206 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.94 ms, Average inference time: 7.14 ms

2025-08-28 15:34:29.207 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:34:29.329 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:34:29.406 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch484
2025-08-28 15:34:32.336 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 484/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 3.8, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.5, lr: 1.814e-04, size: 512, ETA: 0:38:29
2025-08-28 15:34:35.373 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 484/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.809e-04, size: 576, ETA: 0:38:26
2025-08-28 15:34:38.364 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 484/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 3.9, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 0.7, cls_loss: 0.6, lr: 1.805e-04, size: 352, ETA: 0:38:23
2025-08-28 15:34:41.475 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 484/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.6, lr: 1.800e-04, size: 544, ETA: 0:38:20
2025-08-28 15:34:44.570 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 484/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.9, lr: 1.795e-04, size: 320, ETA: 0:38:17
2025-08-28 15:34:47.486 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 484/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 1.5, conf_loss: 2.7, cls_loss: 0.7, lr: 1.791e-04, size: 576, ETA: 0:38:14
2025-08-28 15:34:48.830 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:34:54.963 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:34:55.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:34:55.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6032
2025-08-28 15:34:56.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5228
2025-08-28 15:34:56.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4234
2025-08-28 15:34:56.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5165
2025-08-28 15:34:56.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:34:56.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:34:56.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.603
2025-08-28 15:34:56.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-08-28 15:34:56.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.423
2025-08-28 15:34:56.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.516
2025-08-28 15:34:56.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:34:56.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:34:56.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:34:56.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:34:56.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:34:56.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:34:56.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:34:56.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:34:56.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:34:56.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:34:57.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:34:57.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:34:58.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:34:58.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:34:59.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:34:59.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:35:00.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:35:00.806 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:35:00.807 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 15:35:00.807 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 15:35:00.807 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:35:00.820 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.89 ms, Average inference time: 7.13 ms

2025-08-28 15:35:00.821 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:35:00.935 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:35:01.008 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch485
2025-08-28 15:35:03.879 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 485/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.6, lr: 1.784e-04, size: 544, ETA: 0:38:09
2025-08-28 15:35:06.881 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 485/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.7, lr: 1.779e-04, size: 384, ETA: 0:38:06
2025-08-28 15:35:09.816 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 485/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 1.775e-04, size: 352, ETA: 0:38:03
2025-08-28 15:35:12.815 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 485/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 1.770e-04, size: 384, ETA: 0:38:00
2025-08-28 15:35:15.862 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 485/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.1, cls_loss: 0.6, lr: 1.765e-04, size: 384, ETA: 0:37:57
2025-08-28 15:35:18.818 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 485/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.7, lr: 1.761e-04, size: 544, ETA: 0:37:54
2025-08-28 15:35:20.294 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:35:26.568 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:35:27.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:35:27.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5828
2025-08-28 15:35:27.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4842
2025-08-28 15:35:27.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3772
2025-08-28 15:35:27.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4814
2025-08-28 15:35:27.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:35:27.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:35:27.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.583
2025-08-28 15:35:27.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-28 15:35:27.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-08-28 15:35:27.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-08-28 15:35:27.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:35:27.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:35:27.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:35:27.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:35:27.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:35:27.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:35:27.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:35:27.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:35:27.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:35:28.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:35:29.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:35:29.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:35:30.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:35:30.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:35:31.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:35:32.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:35:32.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:35:33.319 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:35:33.319 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:35:33.319 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 15:35:33.319 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:35:33.326 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.08 ms, Average NMS time: 0.93 ms, Average inference time: 7.01 ms

2025-08-28 15:35:33.327 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:35:33.413 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:35:33.493 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch486
2025-08-28 15:35:36.532 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 486/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 3.4, cls_loss: 0.9, lr: 1.754e-04, size: 256, ETA: 0:37:49
2025-08-28 15:35:39.587 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 486/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.6, lr: 1.750e-04, size: 480, ETA: 0:37:46
2025-08-28 15:35:42.591 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 486/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.7, lr: 1.745e-04, size: 448, ETA: 0:37:43
2025-08-28 15:35:45.544 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 486/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.740e-04, size: 480, ETA: 0:37:40
2025-08-28 15:35:48.611 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 486/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 1.736e-04, size: 320, ETA: 0:37:37
2025-08-28 15:35:51.756 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 486/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.6, lr: 1.731e-04, size: 544, ETA: 0:37:34
2025-08-28 15:35:53.251 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:35:59.525 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:36:00.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:36:00.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5934
2025-08-28 15:36:01.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5366
2025-08-28 15:36:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3998
2025-08-28 15:36:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5099
2025-08-28 15:36:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:36:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:36:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-08-28 15:36:01.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-08-28 15:36:01.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-28 15:36:01.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.510
2025-08-28 15:36:01.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:36:01.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:36:01.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:36:01.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:36:01.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:36:01.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:36:01.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:36:01.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:36:01.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:36:01.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:36:02.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:36:03.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:36:03.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:36:04.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:36:05.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:36:06.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:36:06.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:36:07.417 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:36:07.418 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:36:07.418 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:36:07.418 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:36:07.425 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.91 ms, Average inference time: 7.14 ms

2025-08-28 15:36:07.426 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:36:07.510 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:36:07.592 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch487
2025-08-28 15:36:10.511 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 487/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.8, lr: 1.725e-04, size: 480, ETA: 0:37:29
2025-08-28 15:36:13.485 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 487/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 1.720e-04, size: 256, ETA: 0:37:26
2025-08-28 15:36:16.642 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 487/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.716e-04, size: 416, ETA: 0:37:23
2025-08-28 15:36:19.573 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 487/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 1.711e-04, size: 416, ETA: 0:37:20
2025-08-28 15:36:22.576 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 487/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 1.0, lr: 1.706e-04, size: 288, ETA: 0:37:17
2025-08-28 15:36:25.596 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 487/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.702e-04, size: 544, ETA: 0:37:14
2025-08-28 15:36:26.948 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:36:33.123 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:36:33.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:36:34.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5912
2025-08-28 15:36:34.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5124
2025-08-28 15:36:34.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3819
2025-08-28 15:36:34.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4951
2025-08-28 15:36:34.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:36:34.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:36:34.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.591
2025-08-28 15:36:34.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-08-28 15:36:34.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-08-28 15:36:34.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-08-28 15:36:34.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:36:34.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:36:34.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:36:34.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:36:34.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:36:34.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:36:34.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:36:34.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:36:34.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:36:35.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:36:35.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:36:36.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:36:36.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:36:37.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:36:37.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:36:38.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:36:38.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:36:39.503 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:36:39.504 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:36:39.504 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:36:39.504 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:36:39.512 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.91 ms, Average inference time: 7.14 ms

2025-08-28 15:36:39.513 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:36:39.600 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:36:39.683 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch488
2025-08-28 15:36:42.649 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 488/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.5, lr: 1.695e-04, size: 480, ETA: 0:37:09
2025-08-28 15:36:45.651 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 488/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 1.691e-04, size: 416, ETA: 0:37:06
2025-08-28 15:36:48.698 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 488/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.3, l1_loss: 1.5, conf_loss: 2.7, cls_loss: 1.3, lr: 1.686e-04, size: 576, ETA: 0:37:03
2025-08-28 15:36:51.820 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 488/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 1.682e-04, size: 256, ETA: 0:37:00
2025-08-28 15:36:54.741 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 488/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.003s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 1.677e-04, size: 448, ETA: 0:36:57
2025-08-28 15:36:57.692 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 488/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 1.1, lr: 1.673e-04, size: 544, ETA: 0:36:54
2025-08-28 15:36:59.092 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:37:05.378 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:37:06.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:37:06.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5921
2025-08-28 15:37:06.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4963
2025-08-28 15:37:06.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3923
2025-08-28 15:37:06.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4936
2025-08-28 15:37:06.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:37:06.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:37:06.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-08-28 15:37:06.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-08-28 15:37:06.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-28 15:37:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-08-28 15:37:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:37:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:37:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:37:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:37:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:37:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:37:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:37:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:37:06.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:37:07.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:37:07.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:37:08.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:37:08.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:37:09.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:37:09.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:37:10.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:37:10.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:37:11.504 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:37:11.504 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:37:11.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:37:11.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:37:11.511 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.93 ms, Average inference time: 7.13 ms

2025-08-28 15:37:11.512 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:37:11.592 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:37:11.669 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch489
2025-08-28 15:37:14.534 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 489/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.7, lr: 1.666e-04, size: 448, ETA: 0:36:50
2025-08-28 15:37:17.638 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 489/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.8, lr: 1.662e-04, size: 576, ETA: 0:36:47
2025-08-28 15:37:20.817 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 489/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 1.657e-04, size: 416, ETA: 0:36:43
2025-08-28 15:37:23.748 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 489/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 1.4, conf_loss: 2.1, cls_loss: 0.8, lr: 1.653e-04, size: 448, ETA: 0:36:40
2025-08-28 15:37:26.775 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 489/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.7, lr: 1.648e-04, size: 512, ETA: 0:36:37
2025-08-28 15:37:29.746 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 489/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.7, lr: 1.644e-04, size: 544, ETA: 0:36:34
2025-08-28 15:37:31.240 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:37:37.461 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:37:38.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:37:38.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5752
2025-08-28 15:37:38.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4988
2025-08-28 15:37:38.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3829
2025-08-28 15:37:38.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4856
2025-08-28 15:37:38.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:37:38.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:37:38.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.575
2025-08-28 15:37:38.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-08-28 15:37:38.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-28 15:37:38.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.486
2025-08-28 15:37:38.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:37:38.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:37:38.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:37:38.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:37:38.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:37:38.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:37:38.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:37:38.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:37:38.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:37:39.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:37:39.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:37:40.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:37:41.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:37:41.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:37:42.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:37:42.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:37:43.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:37:44.051 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:37:44.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:37:44.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:37:44.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:37:44.059 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.40 ms, Average NMS time: 0.94 ms, Average inference time: 7.34 ms

2025-08-28 15:37:44.060 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:37:44.143 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:37:44.232 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch490
2025-08-28 15:37:47.012 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 490/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.137s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.637e-04, size: 448, ETA: 0:36:30
2025-08-28 15:37:50.008 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 490/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 3.1, iou_loss: 1.3, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.4, lr: 1.633e-04, size: 416, ETA: 0:36:27
2025-08-28 15:37:53.027 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 490/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 1.629e-04, size: 320, ETA: 0:36:24
2025-08-28 15:37:55.966 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 490/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.8, cls_loss: 1.0, lr: 1.624e-04, size: 480, ETA: 0:36:20
2025-08-28 15:37:58.958 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 490/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 1.620e-04, size: 384, ETA: 0:36:17
2025-08-28 15:38:01.964 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 490/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 8.6, iou_loss: 3.2, l1_loss: 0.9, conf_loss: 3.9, cls_loss: 0.7, lr: 1.615e-04, size: 352, ETA: 0:36:14
2025-08-28 15:38:03.310 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:38:09.527 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:38:10.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:38:10.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6090
2025-08-28 15:38:11.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5189
2025-08-28 15:38:11.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4114
2025-08-28 15:38:11.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5131
2025-08-28 15:38:11.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:38:11.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:38:11.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-08-28 15:38:11.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.519
2025-08-28 15:38:11.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-08-28 15:38:11.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-08-28 15:38:11.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:38:11.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:38:11.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:38:11.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:38:11.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:38:11.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:38:11.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:38:11.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:38:11.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:38:12.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:38:12.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:38:13.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:38:14.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:38:15.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:38:15.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:38:16.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:38:17.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:38:17.925 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:38:17.925 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 15:38:17.925 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:38:17.925 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:38:17.933 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.93 ms, Average inference time: 7.23 ms

2025-08-28 15:38:17.934 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:38:18.012 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:38:18.133 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch491
2025-08-28 15:38:20.964 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 491/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 1.609e-04, size: 256, ETA: 0:36:10
2025-08-28 15:38:23.974 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 491/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 1.604e-04, size: 512, ETA: 0:36:07
2025-08-28 15:38:27.089 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 491/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 1.600e-04, size: 576, ETA: 0:36:04
2025-08-28 15:38:30.162 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 491/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 1.596e-04, size: 480, ETA: 0:36:00
2025-08-28 15:38:33.334 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 491/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.591e-04, size: 512, ETA: 0:35:57
2025-08-28 15:38:36.426 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 491/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 1.587e-04, size: 512, ETA: 0:35:54
2025-08-28 15:38:37.788 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:38:43.956 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:38:44.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:38:44.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5759
2025-08-28 15:38:45.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5015
2025-08-28 15:38:45.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3855
2025-08-28 15:38:45.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4876
2025-08-28 15:38:45.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:38:45.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:38:45.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-08-28 15:38:45.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-08-28 15:38:45.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.386
2025-08-28 15:38:45.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-08-28 15:38:45.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:38:45.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:38:45.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:38:45.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:38:45.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:38:45.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:38:45.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:38:45.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:38:45.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:38:45.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:38:46.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:38:46.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:38:47.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:38:47.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:38:48.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:38:48.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:38:49.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:38:49.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:38:49.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:38:49.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:38:49.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:38:49.742 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.91 ms, Average inference time: 7.02 ms

2025-08-28 15:38:49.743 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:38:49.865 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:38:49.938 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch492
2025-08-28 15:38:52.795 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 492/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.6, lr: 1.581e-04, size: 512, ETA: 0:35:50
2025-08-28 15:38:55.833 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 492/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 1.576e-04, size: 544, ETA: 0:35:47
2025-08-28 15:38:58.852 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 492/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.8, lr: 1.572e-04, size: 480, ETA: 0:35:44
2025-08-28 15:39:01.838 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 492/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 1.567e-04, size: 512, ETA: 0:35:41
2025-08-28 15:39:04.895 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 492/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.5, lr: 1.563e-04, size: 352, ETA: 0:35:38
2025-08-28 15:39:07.914 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 492/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.5, lr: 1.559e-04, size: 288, ETA: 0:35:34
2025-08-28 15:39:09.358 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:39:15.523 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:39:16.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:39:16.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5944
2025-08-28 15:39:16.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5224
2025-08-28 15:39:16.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3441
2025-08-28 15:39:16.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4870
2025-08-28 15:39:16.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:39:16.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:39:16.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.594
2025-08-28 15:39:16.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-08-28 15:39:16.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.344
2025-08-28 15:39:16.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-08-28 15:39:16.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:39:16.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:39:16.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:39:16.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:39:16.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:39:16.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:39:16.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:39:16.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:39:16.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:39:17.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:39:18.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:39:18.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:39:19.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:39:19.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:39:20.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:39:21.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:39:21.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:39:22.298 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:39:22.298 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:39:22.298 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:39:22.299 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:39:22.305 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.89 ms, Average inference time: 7.10 ms

2025-08-28 15:39:22.306 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:39:22.389 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:39:22.467 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch493
2025-08-28 15:39:25.319 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 493/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.552e-04, size: 352, ETA: 0:35:30
2025-08-28 15:39:28.304 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 493/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 1.548e-04, size: 448, ETA: 0:35:27
2025-08-28 15:39:31.430 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 493/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.6, lr: 1.544e-04, size: 384, ETA: 0:35:24
2025-08-28 15:39:34.375 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 493/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 2.7, iou_loss: 1.2, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.4, lr: 1.539e-04, size: 480, ETA: 0:35:21
2025-08-28 15:39:37.513 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 493/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.8, lr: 1.535e-04, size: 288, ETA: 0:35:18
2025-08-28 15:39:40.465 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 493/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.8, lr: 1.531e-04, size: 288, ETA: 0:35:15
2025-08-28 15:39:41.982 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:39:48.142 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:39:48.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:39:49.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5679
2025-08-28 15:39:49.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4832
2025-08-28 15:39:49.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3548
2025-08-28 15:39:49.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4686
2025-08-28 15:39:49.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:39:49.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:39:49.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.568
2025-08-28 15:39:49.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.483
2025-08-28 15:39:49.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.355
2025-08-28 15:39:49.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.469
2025-08-28 15:39:49.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:39:49.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:39:49.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:39:49.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:39:49.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:39:49.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:39:49.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:39:49.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:39:49.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:39:49.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:39:50.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:39:50.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:39:51.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:39:51.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:39:52.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:39:52.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:39:53.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:39:53.588 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:39:53.589 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:39:53.589 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 15:39:53.589 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:39:53.596 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.90 ms, Average inference time: 7.11 ms

2025-08-28 15:39:53.597 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:39:53.683 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:39:53.765 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch494
2025-08-28 15:39:56.644 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 494/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 1.524e-04, size: 256, ETA: 0:35:10
2025-08-28 15:39:59.547 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 494/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.8, lr: 1.520e-04, size: 352, ETA: 0:35:07
2025-08-28 15:40:02.546 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 494/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.8, lr: 1.516e-04, size: 512, ETA: 0:35:04
2025-08-28 15:40:05.661 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 494/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.8, lr: 1.512e-04, size: 576, ETA: 0:35:01
2025-08-28 15:40:08.749 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 494/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.6, lr: 1.507e-04, size: 448, ETA: 0:34:58
2025-08-28 15:40:11.797 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 494/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.7, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 1.503e-04, size: 512, ETA: 0:34:55
2025-08-28 15:40:13.163 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:40:19.333 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:40:20.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:40:21.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5944
2025-08-28 15:40:21.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5194
2025-08-28 15:40:21.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4269
2025-08-28 15:40:21.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5136
2025-08-28 15:40:21.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:40:21.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:40:21.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.594
2025-08-28 15:40:21.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.519
2025-08-28 15:40:21.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.427
2025-08-28 15:40:21.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-08-28 15:40:21.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:40:21.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:40:21.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:40:21.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:40:21.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:40:21.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:40:21.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:40:21.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:40:21.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:40:22.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:40:22.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:40:23.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:40:24.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:40:25.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:40:26.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:40:26.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:40:27.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:40:28.427 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:40:28.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:40:28.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:40:28.428 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:40:28.435 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.94 ms, Average inference time: 7.23 ms

2025-08-28 15:40:28.436 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:40:28.511 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:40:28.593 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch495
2025-08-28 15:40:31.585 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 495/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.1, l1_loss: 0.5, conf_loss: 2.8, cls_loss: 0.5, lr: 1.497e-04, size: 352, ETA: 0:34:50
2025-08-28 15:40:34.620 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 495/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.6, lr: 1.493e-04, size: 480, ETA: 0:34:47
2025-08-28 15:40:37.654 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 495/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.488e-04, size: 320, ETA: 0:34:44
2025-08-28 15:40:40.746 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 495/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.5, lr: 1.484e-04, size: 288, ETA: 0:34:41
2025-08-28 15:40:43.769 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 495/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 1.480e-04, size: 256, ETA: 0:34:38
2025-08-28 15:40:46.793 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 495/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.476e-04, size: 256, ETA: 0:34:35
2025-08-28 15:40:48.193 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:40:54.320 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:40:54.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:40:55.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5844
2025-08-28 15:40:55.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4937
2025-08-28 15:40:55.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3640
2025-08-28 15:40:55.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4807
2025-08-28 15:40:55.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:40:55.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:40:55.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-08-28 15:40:55.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-08-28 15:40:55.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-08-28 15:40:55.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-08-28 15:40:55.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:40:55.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:40:55.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:40:55.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:40:55.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:40:55.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:40:55.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:40:55.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:40:55.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:40:55.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:40:56.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:40:56.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:40:57.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:40:57.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:40:58.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:40:58.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:40:59.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:40:59.873 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:40:59.873 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:40:59.873 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 15:40:59.873 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:40:59.880 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.87 ms, Average inference time: 7.08 ms

2025-08-28 15:40:59.881 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:41:00.006 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:41:00.081 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch496
2025-08-28 15:41:03.030 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 496/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.1, cls_loss: 0.6, lr: 1.469e-04, size: 576, ETA: 0:34:30
2025-08-28 15:41:06.182 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 496/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.2, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.5, lr: 1.465e-04, size: 576, ETA: 0:34:27
2025-08-28 15:41:09.346 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 496/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 1.461e-04, size: 512, ETA: 0:34:24
2025-08-28 15:41:12.320 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 496/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.7, lr: 1.457e-04, size: 512, ETA: 0:34:21
2025-08-28 15:41:15.319 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 496/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.452e-04, size: 448, ETA: 0:34:18
2025-08-28 15:41:18.396 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 496/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.8, lr: 1.448e-04, size: 544, ETA: 0:34:15
2025-08-28 15:41:19.793 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:41:25.980 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:41:26.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:41:26.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5833
2025-08-28 15:41:27.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5222
2025-08-28 15:41:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3713
2025-08-28 15:41:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4923
2025-08-28 15:41:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:41:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:41:27.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.583
2025-08-28 15:41:27.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-08-28 15:41:27.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-08-28 15:41:27.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-08-28 15:41:27.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:41:27.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:41:27.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:41:27.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:41:27.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:41:27.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:41:27.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:41:27.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:41:27.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:41:27.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:41:28.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:41:28.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:41:29.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:41:29.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:41:30.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:41:30.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:41:30.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:41:31.435 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:41:31.436 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:41:31.436 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:41:31.437 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:41:31.449 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.91 ms, Average inference time: 7.07 ms

2025-08-28 15:41:31.450 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:41:31.623 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:41:31.734 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch497
2025-08-28 15:41:34.769 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 497/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.442e-04, size: 512, ETA: 0:34:11
2025-08-28 15:41:37.930 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 497/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.7, lr: 1.438e-04, size: 384, ETA: 0:34:08
2025-08-28 15:41:40.918 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 497/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.9, lr: 1.434e-04, size: 256, ETA: 0:34:04
2025-08-28 15:41:43.938 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 497/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.5, lr: 1.430e-04, size: 576, ETA: 0:34:01
2025-08-28 15:41:47.037 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 497/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.1, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.5, lr: 1.425e-04, size: 544, ETA: 0:33:58
2025-08-28 15:41:50.159 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 497/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 2.7, cls_loss: 0.7, lr: 1.421e-04, size: 576, ETA: 0:33:55
2025-08-28 15:41:51.586 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:41:57.769 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:41:58.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:41:59.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5903
2025-08-28 15:41:59.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5061
2025-08-28 15:41:59.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3870
2025-08-28 15:41:59.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4945
2025-08-28 15:41:59.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:41:59.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:41:59.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.590
2025-08-28 15:41:59.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-08-28 15:41:59.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-08-28 15:41:59.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-08-28 15:41:59.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:41:59.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:41:59.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:41:59.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:41:59.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:41:59.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:41:59.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:41:59.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:41:59.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:42:00.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:42:00.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:42:01.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:42:02.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:42:03.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:42:03.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:42:04.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:42:05.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:42:05.849 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:42:05.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:42:05.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:42:05.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:42:05.857 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.95 ms, Average inference time: 7.13 ms

2025-08-28 15:42:05.858 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:42:05.943 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:42:06.023 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch498
2025-08-28 15:42:09.050 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 498/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.415e-04, size: 320, ETA: 0:33:51
2025-08-28 15:42:11.947 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 498/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 1.5, cls_loss: 0.5, lr: 1.411e-04, size: 288, ETA: 0:33:48
2025-08-28 15:42:14.879 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 498/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 1.407e-04, size: 352, ETA: 0:33:45
2025-08-28 15:42:17.901 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 498/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.5, lr: 1.403e-04, size: 320, ETA: 0:33:41
2025-08-28 15:42:20.993 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 498/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.006s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.399e-04, size: 576, ETA: 0:33:38
2025-08-28 15:42:24.200 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 498/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.7, lr: 1.394e-04, size: 416, ETA: 0:33:35
2025-08-28 15:42:25.510 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:42:31.714 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:42:32.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:42:33.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5994
2025-08-28 15:42:33.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5264
2025-08-28 15:42:33.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4254
2025-08-28 15:42:33.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5171
2025-08-28 15:42:33.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:42:33.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:42:33.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-08-28 15:42:33.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-08-28 15:42:33.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-08-28 15:42:33.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.517
2025-08-28 15:42:33.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:42:33.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:42:33.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:42:33.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:42:33.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:42:33.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:42:33.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:42:33.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:42:33.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:42:34.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:42:34.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:42:35.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:42:36.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:42:37.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:42:37.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:42:38.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:42:39.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:42:39.926 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:42:39.926 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:42:39.926 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 15:42:39.927 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:42:39.934 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.93 ms, Average inference time: 7.10 ms

2025-08-28 15:42:39.935 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:42:40.067 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:42:40.139 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch499
2025-08-28 15:42:43.139 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 499/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.388e-04, size: 320, ETA: 0:33:31
2025-08-28 15:42:46.175 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 499/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.8, lr: 1.384e-04, size: 352, ETA: 0:33:28
2025-08-28 15:42:49.190 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 499/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.380e-04, size: 448, ETA: 0:33:25
2025-08-28 15:42:52.190 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 499/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.6, lr: 1.376e-04, size: 352, ETA: 0:33:22
2025-08-28 15:42:55.245 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 499/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.6, lr: 1.372e-04, size: 576, ETA: 0:33:19
2025-08-28 15:42:58.385 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 499/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 15.9, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 15.9, cls_loss: 0.0, lr: 1.368e-04, size: 384, ETA: 0:33:16
2025-08-28 15:42:59.769 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:43:05.854 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:43:06.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:43:07.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5983
2025-08-28 15:43:07.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5015
2025-08-28 15:43:07.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3931
2025-08-28 15:43:07.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4977
2025-08-28 15:43:07.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:43:07.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:43:07.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-08-28 15:43:07.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-08-28 15:43:07.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-08-28 15:43:07.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.498
2025-08-28 15:43:07.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:43:07.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:43:07.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:43:07.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:43:07.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:43:07.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:43:07.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:43:07.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:43:07.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:43:08.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:43:08.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:43:09.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:43:10.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:43:10.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:43:11.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:43:12.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:43:13.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:43:13.758 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:43:13.758 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:43:13.758 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:43:13.758 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:43:13.765 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.93 ms, Average inference time: 7.14 ms

2025-08-28 15:43:13.766 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:43:13.847 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:43:13.930 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch500
2025-08-28 15:43:16.710 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 500/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.136s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.6, lr: 1.362e-04, size: 576, ETA: 0:33:11
2025-08-28 15:43:19.818 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 500/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.8, lr: 1.358e-04, size: 352, ETA: 0:33:08
2025-08-28 15:43:22.955 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 500/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.3, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.4, lr: 1.354e-04, size: 416, ETA: 0:33:05
2025-08-28 15:43:26.046 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 500/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.5, cls_loss: 0.6, lr: 1.350e-04, size: 480, ETA: 0:33:02
2025-08-28 15:43:29.110 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 500/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.3, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 1.2, lr: 1.346e-04, size: 480, ETA: 0:32:59
2025-08-28 15:43:32.100 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 500/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 1.342e-04, size: 448, ETA: 0:32:56
2025-08-28 15:43:33.420 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:43:39.535 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:43:40.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:43:40.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6036
2025-08-28 15:43:40.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5351
2025-08-28 15:43:40.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4112
2025-08-28 15:43:40.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5166
2025-08-28 15:43:40.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:43:40.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:43:40.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-08-28 15:43:40.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-08-28 15:43:40.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-08-28 15:43:40.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.517
2025-08-28 15:43:40.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:43:40.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:43:40.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:43:40.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:43:40.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:43:40.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:43:40.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:43:40.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:43:40.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:43:41.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:43:42.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:43:42.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:43:43.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:43:43.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:43:44.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:43:45.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:43:45.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:43:46.185 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:43:46.186 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.28
2025-08-28 15:43:46.186 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 15:43:46.186 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:43:46.193 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.91 ms, Average inference time: 7.08 ms

2025-08-28 15:43:46.194 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:43:46.275 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:43:46.397 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch501
2025-08-28 15:43:49.314 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 501/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.336e-04, size: 256, ETA: 0:32:51
2025-08-28 15:43:52.364 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 501/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.8, lr: 1.332e-04, size: 416, ETA: 0:32:48
2025-08-28 15:43:55.412 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 501/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.2, cls_loss: 0.6, lr: 1.328e-04, size: 448, ETA: 0:32:45
2025-08-28 15:43:58.369 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 501/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 3.2, cls_loss: 0.7, lr: 1.324e-04, size: 256, ETA: 0:32:42
2025-08-28 15:44:01.371 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 501/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 1.320e-04, size: 512, ETA: 0:32:39
2025-08-28 15:44:04.424 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 501/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 1.315e-04, size: 480, ETA: 0:32:36
2025-08-28 15:44:05.751 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:44:11.970 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:44:13.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:44:14.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5924
2025-08-28 15:44:14.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5160
2025-08-28 15:44:14.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3791
2025-08-28 15:44:14.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4958
2025-08-28 15:44:14.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:44:14.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:44:14.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-08-28 15:44:14.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-08-28 15:44:14.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-08-28 15:44:14.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.496
2025-08-28 15:44:14.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:44:14.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:44:14.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:44:14.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:44:14.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:44:14.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:44:14.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:44:14.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:44:14.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:44:15.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:44:16.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:44:17.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:44:18.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:44:19.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:44:20.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:44:21.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:44:22.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:44:23.927 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:44:23.928 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:44:23.928 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:44:23.928 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:44:23.935 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.92 ms, Average inference time: 7.16 ms

2025-08-28 15:44:23.936 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:44:24.073 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:44:24.145 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch502
2025-08-28 15:44:27.085 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 502/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.8, cls_loss: 0.6, lr: 1.310e-04, size: 384, ETA: 0:32:31
2025-08-28 15:44:30.078 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 502/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 1.306e-04, size: 352, ETA: 0:32:28
2025-08-28 15:44:33.141 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 502/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 1.302e-04, size: 352, ETA: 0:32:25
2025-08-28 15:44:36.071 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 502/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 1.298e-04, size: 320, ETA: 0:32:22
2025-08-28 15:44:39.381 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 502/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.294e-04, size: 384, ETA: 0:32:19
2025-08-28 15:44:42.389 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 502/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 7.9, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 1.1, lr: 1.290e-04, size: 256, ETA: 0:32:16
2025-08-28 15:44:43.747 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:44:50.009 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:44:50.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:44:51.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5947
2025-08-28 15:44:51.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5091
2025-08-28 15:44:51.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4066
2025-08-28 15:44:51.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5035
2025-08-28 15:44:51.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:44:51.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:44:51.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.595
2025-08-28 15:44:51.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-08-28 15:44:51.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-28 15:44:51.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-08-28 15:44:51.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:44:51.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:44:51.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:44:51.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:44:51.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:44:51.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:44:51.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:44:51.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:44:51.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:44:52.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:44:52.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:44:53.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:44:53.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:44:54.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:44:55.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:44:55.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:44:56.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:44:56.978 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:44:56.978 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 15:44:56.978 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:44:56.978 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:44:56.986 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.87 ms, Average inference time: 7.18 ms

2025-08-28 15:44:56.987 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:44:57.069 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:44:57.147 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch503
2025-08-28 15:45:00.033 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 503/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 1.284e-04, size: 320, ETA: 0:32:11
2025-08-28 15:45:03.026 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 503/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 14.4, iou_loss: 4.1, l1_loss: 1.8, conf_loss: 7.7, cls_loss: 0.8, lr: 1.280e-04, size: 448, ETA: 0:32:08
2025-08-28 15:45:06.068 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 503/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 0.9, lr: 1.276e-04, size: 576, ETA: 0:32:05
2025-08-28 15:45:09.071 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 503/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 1.272e-04, size: 384, ETA: 0:32:02
2025-08-28 15:45:12.099 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 503/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 9.4, iou_loss: 2.8, l1_loss: 1.6, conf_loss: 4.1, cls_loss: 0.9, lr: 1.268e-04, size: 544, ETA: 0:31:59
2025-08-28 15:45:15.237 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 503/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 1.264e-04, size: 576, ETA: 0:31:56
2025-08-28 15:45:16.635 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:45:22.816 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:45:24.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:45:24.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5900
2025-08-28 15:45:25.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4777
2025-08-28 15:45:25.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3921
2025-08-28 15:45:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4866
2025-08-28 15:45:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:45:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:45:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.590
2025-08-28 15:45:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.478
2025-08-28 15:45:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-28 15:45:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-08-28 15:45:25.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:45:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:45:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:45:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:45:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:45:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:45:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:45:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:45:25.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:45:26.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:45:27.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:45:28.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:45:29.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:45:30.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:45:31.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:45:32.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:45:33.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:45:34.718 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:45:34.718 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:45:34.718 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:45:34.718 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:45:34.726 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.93 ms, Average inference time: 7.23 ms

2025-08-28 15:45:34.727 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:45:34.813 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:45:34.908 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch504
2025-08-28 15:45:37.825 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 504/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.2, cls_loss: 0.6, lr: 1.258e-04, size: 512, ETA: 0:31:52
2025-08-28 15:45:40.732 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 504/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 1.254e-04, size: 352, ETA: 0:31:49
2025-08-28 15:45:43.914 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 504/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 1.250e-04, size: 576, ETA: 0:31:45
2025-08-28 15:45:46.999 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 504/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.247e-04, size: 512, ETA: 0:31:42
2025-08-28 15:45:50.074 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 504/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.243e-04, size: 384, ETA: 0:31:39
2025-08-28 15:45:53.031 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 504/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.8, lr: 1.239e-04, size: 320, ETA: 0:31:36
2025-08-28 15:45:54.365 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:46:00.566 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:46:01.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:46:01.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6092
2025-08-28 15:46:02.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5248
2025-08-28 15:46:02.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3857
2025-08-28 15:46:02.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5066
2025-08-28 15:46:02.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:46:02.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:46:02.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-08-28 15:46:02.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 15:46:02.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.386
2025-08-28 15:46:02.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.507
2025-08-28 15:46:02.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:46:02.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:46:02.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:46:02.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:46:02.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:46:02.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:46:02.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:46:02.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:46:02.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:46:02.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:46:03.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:46:04.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:46:04.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:46:05.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:46:06.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:46:07.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:46:07.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:46:08.413 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:46:08.414 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:46:08.414 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:46:08.414 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:46:08.421 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.10 ms, Average NMS time: 0.94 ms, Average inference time: 7.04 ms

2025-08-28 15:46:08.422 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:46:08.508 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:46:08.585 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch505
2025-08-28 15:46:11.532 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 505/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.1, cls_loss: 0.6, lr: 1.233e-04, size: 576, ETA: 0:31:32
2025-08-28 15:46:14.643 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 505/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 1.229e-04, size: 512, ETA: 0:31:29
2025-08-28 15:46:17.676 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 505/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.8, lr: 1.225e-04, size: 352, ETA: 0:31:26
2025-08-28 15:46:20.620 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 505/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.8, lr: 1.221e-04, size: 384, ETA: 0:31:23
2025-08-28 15:46:23.595 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 505/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 1.217e-04, size: 256, ETA: 0:31:19
2025-08-28 15:46:26.756 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 505/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.6, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 1.214e-04, size: 512, ETA: 0:31:16
2025-08-28 15:46:28.154 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:46:34.266 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:46:34.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:46:35.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5778
2025-08-28 15:46:35.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5188
2025-08-28 15:46:35.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3753
2025-08-28 15:46:35.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4906
2025-08-28 15:46:35.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:46:35.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:46:35.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-08-28 15:46:35.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.519
2025-08-28 15:46:35.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-08-28 15:46:35.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.491
2025-08-28 15:46:35.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:46:35.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:46:35.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:46:35.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:46:35.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:46:35.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:46:35.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:46:35.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:46:35.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:46:35.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:46:36.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:46:36.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:46:37.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:46:37.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:46:38.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:46:39.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:46:39.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:46:40.049 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:46:40.049 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:46:40.049 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:46:40.050 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:46:40.056 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.89 ms, Average inference time: 7.16 ms

2025-08-28 15:46:40.058 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:46:40.137 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:46:40.219 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch506
2025-08-28 15:46:43.109 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 506/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 3.1, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.5, lr: 1.208e-04, size: 480, ETA: 0:31:12
2025-08-28 15:46:46.137 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 506/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 1.1, lr: 1.204e-04, size: 256, ETA: 0:31:09
2025-08-28 15:46:49.267 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 506/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.5, lr: 1.200e-04, size: 352, ETA: 0:31:06
2025-08-28 15:46:52.521 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 506/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 1.196e-04, size: 288, ETA: 0:31:03
2025-08-28 15:46:55.710 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 506/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 1.193e-04, size: 352, ETA: 0:31:00
2025-08-28 15:46:58.728 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 506/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.189e-04, size: 544, ETA: 0:30:57
2025-08-28 15:47:00.111 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:47:06.215 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:47:06.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:47:07.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5909
2025-08-28 15:47:07.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5448
2025-08-28 15:47:07.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3661
2025-08-28 15:47:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5006
2025-08-28 15:47:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:47:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:47:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.591
2025-08-28 15:47:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-08-28 15:47:07.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-08-28 15:47:07.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-08-28 15:47:07.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:47:07.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:47:07.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:47:07.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:47:07.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:47:07.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:47:07.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:47:07.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:47:07.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:47:08.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:47:08.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:47:09.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:47:09.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:47:10.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:47:10.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:47:11.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:47:11.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:47:12.448 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:47:12.449 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:47:12.449 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:47:12.449 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:47:12.456 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.93 ms, Average inference time: 7.07 ms

2025-08-28 15:47:12.457 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:47:12.537 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:47:12.614 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch507
2025-08-28 15:47:15.613 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 507/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.183e-04, size: 256, ETA: 0:30:52
2025-08-28 15:47:18.708 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 507/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 1.179e-04, size: 416, ETA: 0:30:49
2025-08-28 15:47:21.721 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 507/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 1.175e-04, size: 448, ETA: 0:30:46
2025-08-28 15:47:24.860 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 507/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 1.1, lr: 1.172e-04, size: 512, ETA: 0:30:43
2025-08-28 15:47:27.887 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 507/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.168e-04, size: 544, ETA: 0:30:40
2025-08-28 15:47:30.841 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 507/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.2, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.5, lr: 1.164e-04, size: 480, ETA: 0:30:37
2025-08-28 15:47:32.215 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:47:38.347 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:47:38.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:47:39.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5817
2025-08-28 15:47:39.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5173
2025-08-28 15:47:39.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3898
2025-08-28 15:47:39.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4963
2025-08-28 15:47:39.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:47:39.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:47:39.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-08-28 15:47:39.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-08-28 15:47:39.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-08-28 15:47:39.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.496
2025-08-28 15:47:39.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:47:39.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:47:39.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:47:39.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:47:39.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:47:39.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:47:39.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:47:39.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:47:39.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:47:39.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:47:40.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:47:40.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:47:41.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:47:41.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:47:42.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:47:42.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:47:43.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:47:43.585 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:47:43.586 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:47:43.586 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:47:43.586 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:47:43.593 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.88 ms, Average inference time: 7.00 ms

2025-08-28 15:47:43.594 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:47:43.725 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:47:43.796 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch508
2025-08-28 15:47:46.672 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 508/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.5, lr: 1.159e-04, size: 288, ETA: 0:30:32
2025-08-28 15:47:49.714 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 508/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 1.155e-04, size: 256, ETA: 0:30:29
2025-08-28 15:47:52.653 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 508/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 9.3, iou_loss: 3.4, l1_loss: 1.3, conf_loss: 3.7, cls_loss: 0.8, lr: 1.151e-04, size: 288, ETA: 0:30:26
2025-08-28 15:47:55.718 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 508/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.7, lr: 1.147e-04, size: 544, ETA: 0:30:23
2025-08-28 15:47:58.774 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 508/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.143e-04, size: 256, ETA: 0:30:20
2025-08-28 15:48:01.681 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 508/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.140e-04, size: 448, ETA: 0:30:17
2025-08-28 15:48:03.073 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:48:09.267 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:48:10.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:48:10.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5924
2025-08-28 15:48:10.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5157
2025-08-28 15:48:10.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3776
2025-08-28 15:48:10.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4952
2025-08-28 15:48:10.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:48:10.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:48:10.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-08-28 15:48:10.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-08-28 15:48:10.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-08-28 15:48:10.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-08-28 15:48:10.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:48:10.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:48:10.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:48:10.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:48:10.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:48:10.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:48:10.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:48:10.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:48:10.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:48:11.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:48:12.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:48:13.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:48:13.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:48:14.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:48:15.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:48:15.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:48:16.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:48:17.181 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:48:17.181 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:48:17.182 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:48:17.182 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:48:17.188 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.94 ms, Average inference time: 7.24 ms

2025-08-28 15:48:17.189 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:48:17.272 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:48:17.352 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch509
2025-08-28 15:48:20.167 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 509/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.138s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 1.134e-04, size: 320, ETA: 0:30:12
2025-08-28 15:48:23.139 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 509/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.7, lr: 1.130e-04, size: 480, ETA: 0:30:09
2025-08-28 15:48:26.198 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 509/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 1.127e-04, size: 256, ETA: 0:30:06
2025-08-28 15:48:29.266 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 509/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 1.123e-04, size: 416, ETA: 0:30:03
2025-08-28 15:48:32.372 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 509/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.119e-04, size: 448, ETA: 0:30:00
2025-08-28 15:48:35.360 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 509/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.116e-04, size: 320, ETA: 0:29:57
2025-08-28 15:48:36.702 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:48:42.863 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:48:43.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:48:44.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5842
2025-08-28 15:48:44.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4878
2025-08-28 15:48:44.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3943
2025-08-28 15:48:44.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4888
2025-08-28 15:48:44.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:48:44.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:48:44.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-08-28 15:48:44.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-08-28 15:48:44.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-08-28 15:48:44.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.489
2025-08-28 15:48:44.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:48:44.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:48:44.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:48:44.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:48:44.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:48:44.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:48:44.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:48:44.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:48:44.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:48:45.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:48:45.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:48:46.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:48:47.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:48:47.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:48:48.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:48:49.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:48:49.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:48:50.507 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:48:50.507 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:48:50.508 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:48:50.508 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:48:50.515 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.91 ms, Average inference time: 7.08 ms

2025-08-28 15:48:50.516 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:48:50.598 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:48:50.679 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch510
2025-08-28 15:48:53.631 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 510/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 10.1, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 5.1, cls_loss: 0.7, lr: 1.110e-04, size: 576, ETA: 0:29:53
2025-08-28 15:48:56.892 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 510/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.8, lr: 1.106e-04, size: 544, ETA: 0:29:50
2025-08-28 15:49:00.109 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 510/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 1.103e-04, size: 320, ETA: 0:29:47
2025-08-28 15:49:03.131 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 510/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 1.099e-04, size: 384, ETA: 0:29:43
2025-08-28 15:49:06.339 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 510/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 1.095e-04, size: 384, ETA: 0:29:40
2025-08-28 15:49:09.537 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 510/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.157s, data_time: 0.004s, total_loss: 3.6, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 0.8, cls_loss: 0.5, lr: 1.092e-04, size: 384, ETA: 0:29:37
2025-08-28 15:49:10.959 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:49:17.047 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:49:17.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:49:18.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6073
2025-08-28 15:49:18.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5111
2025-08-28 15:49:18.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4045
2025-08-28 15:49:18.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5076
2025-08-28 15:49:18.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:49:18.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:49:18.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-08-28 15:49:18.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-08-28 15:49:18.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.404
2025-08-28 15:49:18.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.508
2025-08-28 15:49:18.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:49:18.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:49:18.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:49:18.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:49:18.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:49:18.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:49:18.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:49:18.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:49:18.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:49:19.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:49:19.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:49:20.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:49:21.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:49:21.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:49:22.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:49:23.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:49:23.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:49:24.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:49:24.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:49:24.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:49:24.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:49:24.490 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.33 ms, Average NMS time: 0.91 ms, Average inference time: 7.24 ms

2025-08-28 15:49:24.491 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:49:24.574 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:49:24.653 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch511
2025-08-28 15:49:27.671 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 511/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 1.086e-04, size: 288, ETA: 0:29:33
2025-08-28 15:49:30.663 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 511/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.7, lr: 1.083e-04, size: 416, ETA: 0:29:30
2025-08-28 15:49:33.597 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 511/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 1.079e-04, size: 448, ETA: 0:29:27
2025-08-28 15:49:36.653 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 511/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 7.6, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.8, cls_loss: 0.7, lr: 1.075e-04, size: 416, ETA: 0:29:24
2025-08-28 15:49:39.686 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 511/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 1.072e-04, size: 288, ETA: 0:29:21
2025-08-28 15:49:42.691 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 511/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.068e-04, size: 544, ETA: 0:29:18
2025-08-28 15:49:44.022 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:49:50.030 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:49:50.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:49:51.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5993
2025-08-28 15:49:51.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5042
2025-08-28 15:49:51.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3820
2025-08-28 15:49:51.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4952
2025-08-28 15:49:51.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:49:51.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:49:51.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-08-28 15:49:51.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-08-28 15:49:51.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-08-28 15:49:51.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-08-28 15:49:51.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:49:51.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:49:51.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:49:51.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:49:51.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:49:51.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:49:51.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:49:51.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:49:51.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:49:51.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:49:52.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:49:52.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:49:53.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:49:54.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:49:54.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:49:55.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:49:55.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:49:56.281 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:49:56.281 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:49:56.281 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:49:56.281 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:49:56.288 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.90 ms, Average inference time: 7.06 ms

2025-08-28 15:49:56.289 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:49:56.369 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:49:56.451 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch512
2025-08-28 15:49:59.353 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 512/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.063e-04, size: 512, ETA: 0:29:13
2025-08-28 15:50:02.377 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 512/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 1.059e-04, size: 480, ETA: 0:29:10
2025-08-28 15:50:05.382 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 512/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 1.055e-04, size: 544, ETA: 0:29:07
2025-08-28 15:50:08.502 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 512/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.052e-04, size: 384, ETA: 0:29:04
2025-08-28 15:50:11.406 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 512/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.7, lr: 1.048e-04, size: 416, ETA: 0:29:01
2025-08-28 15:50:14.480 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 512/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 3.6, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.5, lr: 1.045e-04, size: 352, ETA: 0:28:58
2025-08-28 15:50:15.834 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:50:22.031 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:50:22.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:50:23.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5992
2025-08-28 15:50:23.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5208
2025-08-28 15:50:23.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3795
2025-08-28 15:50:23.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4998
2025-08-28 15:50:23.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:50:23.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:50:23.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-08-28 15:50:23.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-08-28 15:50:23.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-08-28 15:50:23.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-08-28 15:50:23.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:50:23.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:50:23.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:50:23.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:50:23.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:50:23.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:50:23.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:50:23.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:50:23.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:50:24.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:50:25.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:50:26.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:50:26.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:50:27.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:50:28.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:50:29.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:50:29.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:50:30.566 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:50:30.566 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:50:30.566 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:50:30.566 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:50:30.574 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.91 ms, Average inference time: 7.08 ms

2025-08-28 15:50:30.575 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:50:30.658 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:50:30.736 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch513
2025-08-28 15:50:33.575 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 513/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.039e-04, size: 448, ETA: 0:28:53
2025-08-28 15:50:36.636 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 513/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 1.8, cls_loss: 0.7, lr: 1.036e-04, size: 480, ETA: 0:28:50
2025-08-28 15:50:39.556 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 513/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 23.7, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 23.7, cls_loss: 0.0, lr: 1.032e-04, size: 320, ETA: 0:28:47
2025-08-28 15:50:42.554 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 513/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.5, lr: 1.028e-04, size: 544, ETA: 0:28:44
2025-08-28 15:50:45.703 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 513/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 0.8, lr: 1.025e-04, size: 576, ETA: 0:28:41
2025-08-28 15:50:48.707 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 513/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 1.021e-04, size: 384, ETA: 0:28:38
2025-08-28 15:50:50.056 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:50:56.174 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:50:56.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:50:56.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5426
2025-08-28 15:50:56.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4904
2025-08-28 15:50:57.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3705
2025-08-28 15:50:57.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4678
2025-08-28 15:50:57.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:50:57.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:50:57.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-08-28 15:50:57.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-28 15:50:57.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-08-28 15:50:57.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.468
2025-08-28 15:50:57.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:50:57.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:50:57.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:50:57.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:50:57.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:50:57.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:50:57.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:50:57.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:50:57.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:50:57.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:50:57.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:50:58.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:50:58.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:50:58.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:50:59.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:50:59.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:51:00.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:51:00.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:51:00.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 15:51:00.374 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-08-28 15:51:00.375 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:51:00.382 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.82 ms, Average inference time: 7.07 ms

2025-08-28 15:51:00.383 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:51:00.496 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:51:00.570 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch514
2025-08-28 15:51:03.587 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 514/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 1.016e-04, size: 544, ETA: 0:28:33
2025-08-28 15:51:06.664 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 514/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.5, lr: 1.013e-04, size: 320, ETA: 0:28:30
2025-08-28 15:51:09.678 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 514/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 1.009e-04, size: 480, ETA: 0:28:27
2025-08-28 15:51:12.792 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 514/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.5, lr: 1.005e-04, size: 448, ETA: 0:28:24
2025-08-28 15:51:15.811 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 514/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.2, cls_loss: 0.6, lr: 1.002e-04, size: 352, ETA: 0:28:21
2025-08-28 15:51:18.744 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 514/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 9.984e-05, size: 448, ETA: 0:28:18
2025-08-28 15:51:20.062 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:51:26.324 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:51:27.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:51:27.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5915
2025-08-28 15:51:27.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5387
2025-08-28 15:51:27.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4072
2025-08-28 15:51:27.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5125
2025-08-28 15:51:27.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:51:27.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:51:27.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.591
2025-08-28 15:51:27.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-08-28 15:51:27.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-28 15:51:27.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.512
2025-08-28 15:51:27.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:51:27.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:51:27.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:51:27.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:51:27.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:51:27.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:51:27.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:51:27.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:51:27.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:51:28.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:51:28.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:51:29.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:51:29.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:51:30.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:51:31.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:51:31.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:51:32.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:51:32.876 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:51:32.876 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 15:51:32.876 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:51:32.876 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:51:32.884 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.92 ms, Average inference time: 7.18 ms

2025-08-28 15:51:32.885 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:51:33.003 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:51:33.112 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch515
2025-08-28 15:51:35.880 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 515/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.136s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.8, cls_loss: 0.5, lr: 9.933e-05, size: 352, ETA: 0:28:14
2025-08-28 15:51:38.892 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 515/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.6, lr: 9.898e-05, size: 448, ETA: 0:28:10
2025-08-28 15:51:41.900 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 515/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 1.7, cls_loss: 0.6, lr: 9.862e-05, size: 576, ETA: 0:28:07
2025-08-28 15:51:45.115 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 515/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 9.827e-05, size: 320, ETA: 0:28:04
2025-08-28 15:51:48.332 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 515/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 9.792e-05, size: 544, ETA: 0:28:01
2025-08-28 15:51:51.520 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 515/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 9.757e-05, size: 512, ETA: 0:27:58
2025-08-28 15:51:52.842 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:51:59.008 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:51:59.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:52:00.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5822
2025-08-28 15:52:00.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5024
2025-08-28 15:52:00.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3912
2025-08-28 15:52:00.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4919
2025-08-28 15:52:00.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:52:00.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:52:00.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-08-28 15:52:00.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-08-28 15:52:00.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-08-28 15:52:00.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-08-28 15:52:00.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:52:00.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:52:00.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:52:00.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:52:00.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:52:00.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:52:00.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:52:00.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:52:00.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:52:00.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:52:01.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:52:02.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:52:02.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:52:03.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:52:03.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:52:04.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:52:04.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:52:05.444 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:52:05.445 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:52:05.445 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:52:05.445 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:52:05.451 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.89 ms, Average inference time: 7.03 ms

2025-08-28 15:52:05.452 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:52:05.571 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:52:05.643 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch516
2025-08-28 15:52:08.678 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 516/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 9.707e-05, size: 512, ETA: 0:27:54
2025-08-28 15:52:11.679 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 516/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.9, lr: 9.672e-05, size: 288, ETA: 0:27:51
2025-08-28 15:52:14.893 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 516/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 9.637e-05, size: 288, ETA: 0:27:48
2025-08-28 15:52:17.996 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 516/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 9.602e-05, size: 576, ETA: 0:27:45
2025-08-28 15:52:21.108 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 516/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 9.567e-05, size: 576, ETA: 0:27:42
2025-08-28 15:52:24.384 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 516/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 9.533e-05, size: 352, ETA: 0:27:39
2025-08-28 15:52:25.700 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:52:31.903 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:52:32.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:52:32.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5803
2025-08-28 15:52:32.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5026
2025-08-28 15:52:32.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3777
2025-08-28 15:52:32.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4869
2025-08-28 15:52:32.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:52:32.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:52:32.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.580
2025-08-28 15:52:32.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-08-28 15:52:32.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-08-28 15:52:32.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-08-28 15:52:32.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:52:32.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:52:32.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:52:32.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:52:32.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:52:32.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:52:32.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:52:32.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:52:32.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:52:33.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:52:33.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:52:34.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:52:34.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:52:34.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:52:35.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:52:35.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:52:36.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:52:36.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:52:36.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:52:36.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:52:36.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:52:36.463 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.86 ms, Average inference time: 7.11 ms

2025-08-28 15:52:36.463 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:52:36.545 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:52:36.622 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch517
2025-08-28 15:52:39.579 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 517/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 9.483e-05, size: 320, ETA: 0:27:34
2025-08-28 15:52:42.503 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 517/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 9.448e-05, size: 416, ETA: 0:27:31
2025-08-28 15:52:45.451 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 517/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 9.414e-05, size: 352, ETA: 0:27:28
2025-08-28 15:52:48.531 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 517/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 9.380e-05, size: 352, ETA: 0:27:25
2025-08-28 15:52:51.605 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 517/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 9.345e-05, size: 480, ETA: 0:27:22
2025-08-28 15:52:54.899 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 517/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.5, lr: 9.311e-05, size: 416, ETA: 0:27:19
2025-08-28 15:52:56.265 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:53:02.458 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:53:03.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:53:03.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5857
2025-08-28 15:53:03.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5301
2025-08-28 15:53:03.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3855
2025-08-28 15:53:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5004
2025-08-28 15:53:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:53:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:53:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-08-28 15:53:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 15:53:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-28 15:53:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-08-28 15:53:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:53:03.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:53:03.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:53:03.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:53:03.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:53:03.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:53:03.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:53:03.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:53:03.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:53:04.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:53:05.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:53:05.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:53:06.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:53:07.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:53:07.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:53:08.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:53:09.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:53:09.766 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:53:09.766 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 15:53:09.766 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:53:09.766 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:53:09.773 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.01 ms, Average NMS time: 0.91 ms, Average inference time: 6.92 ms

2025-08-28 15:53:09.775 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:53:09.867 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:53:09.946 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch518
2025-08-28 15:53:12.891 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 518/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.7, lr: 9.261e-05, size: 576, ETA: 0:27:14
2025-08-28 15:53:16.197 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 518/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.6, lr: 9.227e-05, size: 448, ETA: 0:27:11
2025-08-28 15:53:19.318 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 518/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.8, lr: 9.193e-05, size: 512, ETA: 0:27:08
2025-08-28 15:53:22.302 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 518/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 9.159e-05, size: 448, ETA: 0:27:05
2025-08-28 15:53:25.322 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 518/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 1.0, lr: 9.125e-05, size: 320, ETA: 0:27:02
2025-08-28 15:53:28.232 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 518/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 9.092e-05, size: 416, ETA: 0:26:59
2025-08-28 15:53:29.659 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:53:35.944 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:53:36.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:53:37.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5965
2025-08-28 15:53:37.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5327
2025-08-28 15:53:37.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4105
2025-08-28 15:53:37.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5132
2025-08-28 15:53:37.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:53:37.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:53:37.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.597
2025-08-28 15:53:37.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-08-28 15:53:37.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-08-28 15:53:37.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-08-28 15:53:37.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:53:37.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:53:37.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:53:37.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:53:37.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:53:37.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:53:37.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:53:37.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:53:37.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:53:38.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:53:38.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:53:39.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:53:40.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:53:40.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:53:41.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:53:41.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:53:42.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:53:43.267 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:53:43.267 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:53:43.267 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:53:43.267 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:53:43.275 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.87 ms, Average inference time: 7.09 ms

2025-08-28 15:53:43.276 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:53:43.389 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:53:43.463 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch519
2025-08-28 15:53:46.484 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 519/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 9.043e-05, size: 480, ETA: 0:26:54
2025-08-28 15:53:49.472 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 519/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 9.009e-05, size: 448, ETA: 0:26:51
2025-08-28 15:53:52.457 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 519/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.6, lr: 8.975e-05, size: 512, ETA: 0:26:48
2025-08-28 15:53:55.537 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 519/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.005s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 8.942e-05, size: 544, ETA: 0:26:45
2025-08-28 15:53:58.547 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 519/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 1.1, cls_loss: 0.5, lr: 8.908e-05, size: 416, ETA: 0:26:42
2025-08-28 15:54:01.481 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 519/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 8.875e-05, size: 512, ETA: 0:26:39
2025-08-28 15:54:02.800 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:54:08.953 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:54:09.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:54:10.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6060
2025-08-28 15:54:10.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5403
2025-08-28 15:54:10.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3676
2025-08-28 15:54:10.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5046
2025-08-28 15:54:10.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:54:10.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:54:10.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.606
2025-08-28 15:54:10.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.540
2025-08-28 15:54:10.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-28 15:54:10.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-08-28 15:54:10.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:54:10.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:54:10.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:54:10.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:54:10.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:54:10.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:54:10.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:54:10.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:54:10.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:54:11.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:54:11.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:54:12.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:54:13.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:54:14.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:54:14.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:54:15.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:54:16.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:54:16.782 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:54:16.783 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:54:16.783 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:54:16.783 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:54:16.790 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.92 ms, Average inference time: 7.05 ms

2025-08-28 15:54:16.791 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:54:16.879 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:54:16.960 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch520
2025-08-28 15:54:19.788 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 520/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 8.826e-05, size: 320, ETA: 0:26:35
2025-08-28 15:54:22.895 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 520/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.9, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.7, lr: 8.793e-05, size: 448, ETA: 0:26:32
2025-08-28 15:54:25.907 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 520/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.8, lr: 8.760e-05, size: 288, ETA: 0:26:28
2025-08-28 15:54:28.893 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 520/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 8.2, iou_loss: 2.0, l1_loss: 1.3, conf_loss: 4.4, cls_loss: 0.5, lr: 8.727e-05, size: 480, ETA: 0:26:25
2025-08-28 15:54:32.006 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 520/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 8.693e-05, size: 352, ETA: 0:26:22
2025-08-28 15:54:34.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 520/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 8.660e-05, size: 512, ETA: 0:26:19
2025-08-28 15:54:36.279 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:54:42.334 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:54:43.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:54:43.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6033
2025-08-28 15:54:43.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5304
2025-08-28 15:54:43.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4064
2025-08-28 15:54:43.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5134
2025-08-28 15:54:43.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:54:43.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:54:43.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.603
2025-08-28 15:54:43.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 15:54:43.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-08-28 15:54:43.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-08-28 15:54:43.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:54:43.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:54:43.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:54:43.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:54:43.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:54:43.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:54:43.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:54:43.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:54:43.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:54:44.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:54:44.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:54:45.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:54:46.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:54:46.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:54:47.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:54:47.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:54:48.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:54:48.916 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:54:48.916 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:54:48.916 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:54:48.916 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:54:48.923 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.95 ms, Average inference time: 7.06 ms

2025-08-28 15:54:48.924 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:54:49.057 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:54:49.128 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch521
2025-08-28 15:54:51.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 521/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 1.9, cls_loss: 0.7, lr: 8.612e-05, size: 544, ETA: 0:26:15
2025-08-28 15:54:54.945 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 521/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.9, lr: 8.580e-05, size: 288, ETA: 0:26:12
2025-08-28 15:54:58.068 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 521/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.6, lr: 8.547e-05, size: 448, ETA: 0:26:09
2025-08-28 15:55:01.266 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 521/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.1, cls_loss: 0.6, lr: 8.514e-05, size: 544, ETA: 0:26:06
2025-08-28 15:55:04.230 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 521/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 8.481e-05, size: 256, ETA: 0:26:02
2025-08-28 15:55:07.280 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 521/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 8.448e-05, size: 384, ETA: 0:25:59
2025-08-28 15:55:08.677 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:55:14.774 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:55:15.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:55:15.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6001
2025-08-28 15:55:16.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5219
2025-08-28 15:55:16.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4113
2025-08-28 15:55:16.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5111
2025-08-28 15:55:16.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:55:16.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:55:16.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-08-28 15:55:16.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-08-28 15:55:16.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-08-28 15:55:16.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-08-28 15:55:16.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:55:16.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:55:16.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:55:16.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:55:16.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:55:16.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:55:16.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:55:16.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:55:16.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:55:16.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:55:17.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:55:17.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:55:18.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:55:19.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:55:19.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:55:20.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:55:20.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:55:21.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:55:21.566 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:55:21.566 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 15:55:21.566 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:55:21.573 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.90 ms, Average inference time: 7.07 ms

2025-08-28 15:55:21.574 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:55:21.655 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:55:21.737 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch522
2025-08-28 15:55:24.624 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 522/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 8.401e-05, size: 352, ETA: 0:25:55
2025-08-28 15:55:27.635 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 522/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 3.7, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 0.6, cls_loss: 0.5, lr: 8.369e-05, size: 448, ETA: 0:25:52
2025-08-28 15:55:30.542 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 522/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 8.336e-05, size: 288, ETA: 0:25:49
2025-08-28 15:55:33.576 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 522/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.8, lr: 8.304e-05, size: 480, ETA: 0:25:46
2025-08-28 15:55:36.536 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 522/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 8.271e-05, size: 256, ETA: 0:25:43
2025-08-28 15:55:39.584 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 522/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 3.0, iou_loss: 1.3, l1_loss: 0.4, conf_loss: 0.8, cls_loss: 0.4, lr: 8.239e-05, size: 576, ETA: 0:25:40
2025-08-28 15:55:40.997 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:55:47.293 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:55:48.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:55:48.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6004
2025-08-28 15:55:48.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5042
2025-08-28 15:55:48.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3853
2025-08-28 15:55:48.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4966
2025-08-28 15:55:48.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:55:48.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:55:48.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-08-28 15:55:48.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-08-28 15:55:48.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-08-28 15:55:48.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-08-28 15:55:48.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:55:48.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:55:48.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:55:48.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:55:48.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:55:48.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:55:48.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:55:48.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:55:48.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:55:49.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:55:50.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:55:50.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:55:51.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:55:52.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:55:52.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:55:53.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:55:53.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:55:54.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:55:54.494 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:55:54.494 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:55:54.494 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:55:54.501 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.92 ms, Average inference time: 7.24 ms

2025-08-28 15:55:54.502 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:55:54.578 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:55:54.657 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch523
2025-08-28 15:55:57.556 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 523/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 8.192e-05, size: 352, ETA: 0:25:35
2025-08-28 15:56:00.519 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 523/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.8, lr: 8.160e-05, size: 352, ETA: 0:25:32
2025-08-28 15:56:03.593 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 523/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.5, lr: 8.128e-05, size: 256, ETA: 0:25:29
2025-08-28 15:56:06.593 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 523/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.8, cls_loss: 0.6, lr: 8.096e-05, size: 256, ETA: 0:25:26
2025-08-28 15:56:09.591 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 523/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 8.064e-05, size: 416, ETA: 0:25:23
2025-08-28 15:56:12.558 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 523/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 8.032e-05, size: 416, ETA: 0:25:20
2025-08-28 15:56:13.886 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:56:20.354 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:56:20.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:56:21.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6008
2025-08-28 15:56:21.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5010
2025-08-28 15:56:21.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3792
2025-08-28 15:56:21.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4937
2025-08-28 15:56:21.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:56:21.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:56:21.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.601
2025-08-28 15:56:21.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-08-28 15:56:21.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-08-28 15:56:21.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-08-28 15:56:21.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:56:21.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:56:21.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:56:21.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:56:21.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:56:21.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:56:21.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:56:21.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:56:21.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:56:22.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:56:22.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:56:23.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:56:23.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:56:24.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:56:24.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:56:25.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:56:25.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:56:26.164 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:56:26.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:56:26.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:56:26.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:56:26.172 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.91 ms, Average inference time: 7.19 ms

2025-08-28 15:56:26.173 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:56:26.310 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:56:26.382 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch524
2025-08-28 15:56:29.237 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 524/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 7.986e-05, size: 448, ETA: 0:25:15
2025-08-28 15:56:32.403 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 524/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 1.2, cls_loss: 0.5, lr: 7.954e-05, size: 320, ETA: 0:25:12
2025-08-28 15:56:35.442 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 524/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 7.923e-05, size: 448, ETA: 0:25:09
2025-08-28 15:56:38.450 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 524/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 3.6, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.5, lr: 7.891e-05, size: 288, ETA: 0:25:06
2025-08-28 15:56:41.526 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 524/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 7.859e-05, size: 576, ETA: 0:25:03
2025-08-28 15:56:44.687 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 524/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 7.828e-05, size: 320, ETA: 0:25:00
2025-08-28 15:56:46.030 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:56:52.206 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:56:52.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:56:53.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5929
2025-08-28 15:56:53.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5276
2025-08-28 15:56:53.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3669
2025-08-28 15:56:53.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4958
2025-08-28 15:56:53.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:56:53.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:56:53.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-08-28 15:56:53.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-08-28 15:56:53.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.367
2025-08-28 15:56:53.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.496
2025-08-28 15:56:53.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:56:53.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:56:53.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:56:53.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:56:53.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:56:53.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:56:53.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:56:53.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:56:53.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:56:54.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:56:55.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:56:55.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:56:56.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:56:56.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:56:57.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:56:58.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:56:58.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:56:59.532 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:56:59.533 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:56:59.533 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:56:59.533 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:56:59.540 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.94 ms, Average inference time: 7.16 ms

2025-08-28 15:56:59.541 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:56:59.619 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:56:59.697 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch525
2025-08-28 15:57:02.532 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 525/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 7.782e-05, size: 448, ETA: 0:24:55
2025-08-28 15:57:05.540 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 525/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 3.2, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 0.9, cls_loss: 0.5, lr: 7.751e-05, size: 384, ETA: 0:24:52
2025-08-28 15:57:08.646 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 525/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.9, lr: 7.720e-05, size: 320, ETA: 0:24:49
2025-08-28 15:57:11.775 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 525/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.8, lr: 7.688e-05, size: 576, ETA: 0:24:46
2025-08-28 15:57:14.880 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 525/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.6, lr: 7.657e-05, size: 416, ETA: 0:24:43
2025-08-28 15:57:17.933 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 525/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.5, lr: 7.626e-05, size: 448, ETA: 0:24:40
2025-08-28 15:57:19.279 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:57:25.488 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:57:26.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:57:26.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5891
2025-08-28 15:57:27.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5171
2025-08-28 15:57:27.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3729
2025-08-28 15:57:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4930
2025-08-28 15:57:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:57:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:57:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-08-28 15:57:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-08-28 15:57:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.373
2025-08-28 15:57:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-08-28 15:57:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:57:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:57:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:57:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:57:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:57:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:57:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:57:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:57:27.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:57:27.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:57:28.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:57:29.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:57:30.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:57:30.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:57:31.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:57:32.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:57:32.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:57:33.601 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:57:33.601 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 15:57:33.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:57:33.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:57:33.614 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.90 ms, Average inference time: 7.14 ms

2025-08-28 15:57:33.615 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:57:33.732 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:57:33.834 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch526
2025-08-28 15:57:36.652 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 526/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 7.581e-05, size: 320, ETA: 0:24:36
2025-08-28 15:57:39.739 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 526/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 2.1, cls_loss: 0.7, lr: 7.550e-05, size: 544, ETA: 0:24:33
2025-08-28 15:57:42.842 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 526/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 7.519e-05, size: 576, ETA: 0:24:30
2025-08-28 15:57:46.021 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 526/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 7.488e-05, size: 448, ETA: 0:24:27
2025-08-28 15:57:49.040 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 526/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 7.457e-05, size: 480, ETA: 0:24:23
2025-08-28 15:57:52.088 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 526/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 7.427e-05, size: 384, ETA: 0:24:20
2025-08-28 15:57:53.467 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:57:59.625 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:58:00.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:58:00.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6019
2025-08-28 15:58:00.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5245
2025-08-28 15:58:00.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4209
2025-08-28 15:58:00.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5158
2025-08-28 15:58:00.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:58:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:58:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.602
2025-08-28 15:58:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 15:58:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.421
2025-08-28 15:58:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.516
2025-08-28 15:58:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:58:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:58:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:58:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:58:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:58:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:58:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:58:00.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:58:00.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:58:01.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:58:02.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:58:02.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:58:03.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:58:03.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:58:04.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:58:04.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:58:05.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:58:06.019 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:58:06.019 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 15:58:06.019 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 15:58:06.020 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:58:06.031 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.91 ms, Average inference time: 7.16 ms

2025-08-28 15:58:06.032 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:58:06.146 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:58:06.238 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch527
2025-08-28 15:58:09.202 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 527/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.5, lr: 7.382e-05, size: 288, ETA: 0:24:16
2025-08-28 15:58:12.293 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 527/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 7.352e-05, size: 384, ETA: 0:24:13
2025-08-28 15:58:15.462 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 527/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.6, lr: 7.321e-05, size: 480, ETA: 0:24:10
2025-08-28 15:58:18.614 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 527/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 7.291e-05, size: 576, ETA: 0:24:07
2025-08-28 15:58:21.917 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 527/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 7.260e-05, size: 416, ETA: 0:24:04
2025-08-28 15:58:24.868 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 527/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 7.230e-05, size: 512, ETA: 0:24:01
2025-08-28 15:58:26.213 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:58:32.390 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:58:33.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:58:33.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5996
2025-08-28 15:58:33.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5259
2025-08-28 15:58:33.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3832
2025-08-28 15:58:33.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5029
2025-08-28 15:58:33.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:58:33.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:58:33.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-08-28 15:58:33.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-08-28 15:58:33.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-28 15:58:33.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-08-28 15:58:33.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:58:33.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:58:33.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:58:33.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:58:33.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:58:33.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:58:33.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:58:33.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:58:33.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:58:34.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:58:35.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:58:36.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:58:36.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:58:37.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:58:38.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:58:38.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:58:39.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:58:40.247 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:58:40.248 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:58:40.248 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 15:58:40.248 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:58:40.255 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.91 ms, Average inference time: 7.18 ms

2025-08-28 15:58:40.256 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:58:40.338 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:58:40.420 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch528
2025-08-28 15:58:43.355 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 528/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 12.1, iou_loss: 3.3, l1_loss: 0.9, conf_loss: 6.7, cls_loss: 1.2, lr: 7.186e-05, size: 544, ETA: 0:23:56
2025-08-28 15:58:46.387 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 528/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 7.156e-05, size: 320, ETA: 0:23:53
2025-08-28 15:58:49.295 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 528/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.5, lr: 7.126e-05, size: 448, ETA: 0:23:50
2025-08-28 15:58:52.300 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 528/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.6, lr: 7.096e-05, size: 288, ETA: 0:23:47
2025-08-28 15:58:55.228 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 528/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.8, lr: 7.066e-05, size: 288, ETA: 0:23:44
2025-08-28 15:58:58.351 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 528/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.5, lr: 7.036e-05, size: 480, ETA: 0:23:41
2025-08-28 15:58:59.765 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:59:05.934 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:59:06.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:59:07.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5780
2025-08-28 15:59:07.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5304
2025-08-28 15:59:07.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3574
2025-08-28 15:59:07.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4886
2025-08-28 15:59:07.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:59:07.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:59:07.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-08-28 15:59:07.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 15:59:07.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.357
2025-08-28 15:59:07.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.489
2025-08-28 15:59:07.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:59:07.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:59:07.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:59:07.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:59:07.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:59:07.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:59:07.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:59:07.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:59:07.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:59:07.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:59:08.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:59:09.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:59:09.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:59:10.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:59:10.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:59:11.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:59:11.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:59:12.430 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:59:12.430 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 15:59:12.431 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 15:59:12.431 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:59:12.445 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.92 ms, Average inference time: 7.16 ms

2025-08-28 15:59:12.446 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:59:12.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:59:12.666 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch529
2025-08-28 15:59:15.527 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 529/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 4.3, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.6, lr: 6.992e-05, size: 416, ETA: 0:23:36
2025-08-28 15:59:18.530 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 529/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.5, lr: 6.963e-05, size: 512, ETA: 0:23:33
2025-08-28 15:59:21.595 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 529/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 0.7, lr: 6.933e-05, size: 512, ETA: 0:23:30
2025-08-28 15:59:24.693 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 529/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 6.903e-05, size: 576, ETA: 0:23:27
2025-08-28 15:59:27.773 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 529/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.7, lr: 6.874e-05, size: 512, ETA: 0:23:24
2025-08-28 15:59:30.829 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 529/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 6.844e-05, size: 288, ETA: 0:23:21
2025-08-28 15:59:32.162 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:59:38.173 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 15:59:38.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 15:59:38.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5610
2025-08-28 15:59:38.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4425
2025-08-28 15:59:38.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3612
2025-08-28 15:59:38.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4549
2025-08-28 15:59:38.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 15:59:38.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 15:59:38.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.561
2025-08-28 15:59:38.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.442
2025-08-28 15:59:38.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-08-28 15:59:38.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.455
2025-08-28 15:59:38.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 15:59:38.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 15:59:38.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 15:59:38.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 15:59:38.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 15:59:38.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 15:59:38.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 15:59:38.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 15:59:38.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 15:59:39.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 15:59:39.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 15:59:39.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 15:59:40.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 15:59:40.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 15:59:40.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 15:59:41.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 15:59:41.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 15:59:41.945 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 15:59:41.945 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 15:59:41.945 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-08-28 15:59:41.945 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 15:59:41.951 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.82 ms, Average inference time: 7.06 ms

2025-08-28 15:59:41.952 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:59:42.040 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 15:59:42.120 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch530
2025-08-28 15:59:45.082 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 530/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 6.801e-05, size: 288, ETA: 0:23:17
2025-08-28 15:59:48.049 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 530/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.6, lr: 6.772e-05, size: 320, ETA: 0:23:14
2025-08-28 15:59:51.111 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 530/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.8, lr: 6.743e-05, size: 544, ETA: 0:23:10
2025-08-28 15:59:54.104 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 530/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.6, lr: 6.713e-05, size: 288, ETA: 0:23:07
2025-08-28 15:59:57.067 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 530/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 6.684e-05, size: 352, ETA: 0:23:04
2025-08-28 16:00:00.170 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 530/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 6.655e-05, size: 352, ETA: 0:23:01
2025-08-28 16:00:01.503 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:00:07.646 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:00:08.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:00:09.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6039
2025-08-28 16:00:09.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5227
2025-08-28 16:00:09.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4084
2025-08-28 16:00:09.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5117
2025-08-28 16:00:09.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:00:09.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:00:09.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-08-28 16:00:09.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-08-28 16:00:09.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.408
2025-08-28 16:00:09.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.512
2025-08-28 16:00:09.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:00:09.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:00:09.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:00:09.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:00:09.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:00:09.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:00:09.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:00:09.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:00:09.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:00:10.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:00:11.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:00:11.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:00:12.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:00:13.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:00:14.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:00:15.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:00:16.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:00:16.916 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:00:16.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:00:16.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:00:16.917 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:00:16.924 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.93 ms, Average inference time: 7.11 ms

2025-08-28 16:00:16.925 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:00:17.041 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:00:17.114 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch531
2025-08-28 16:00:19.940 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 531/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 6.613e-05, size: 256, ETA: 0:22:57
2025-08-28 16:00:23.102 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 531/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 6.584e-05, size: 576, ETA: 0:22:54
2025-08-28 16:00:26.149 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 531/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.5, lr: 6.555e-05, size: 512, ETA: 0:22:51
2025-08-28 16:00:29.352 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 531/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 1.4, conf_loss: 1.5, cls_loss: 0.6, lr: 6.526e-05, size: 576, ETA: 0:22:48
2025-08-28 16:00:32.395 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 531/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 6.497e-05, size: 320, ETA: 0:22:45
2025-08-28 16:00:35.456 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 531/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 6.469e-05, size: 352, ETA: 0:22:41
2025-08-28 16:00:36.824 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:00:42.996 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:00:44.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:00:44.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5838
2025-08-28 16:00:44.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4961
2025-08-28 16:00:44.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3920
2025-08-28 16:00:44.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4906
2025-08-28 16:00:44.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:00:44.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:00:44.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-08-28 16:00:44.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-08-28 16:00:44.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-28 16:00:44.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.491
2025-08-28 16:00:44.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:00:44.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:00:44.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:00:44.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:00:44.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:00:44.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:00:44.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:00:44.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:00:44.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:00:45.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:00:46.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:00:47.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:00:48.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:00:49.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:00:50.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:00:50.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:00:51.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:00:52.857 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:00:52.858 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 16:00:52.858 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 16:00:52.858 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:00:52.865 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.90 ms, Average inference time: 7.13 ms

2025-08-28 16:00:52.866 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:00:52.952 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:00:53.034 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch532
2025-08-28 16:00:55.866 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 532/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 6.427e-05, size: 352, ETA: 0:22:37
2025-08-28 16:00:58.975 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 532/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 6.398e-05, size: 480, ETA: 0:22:34
2025-08-28 16:01:02.094 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 532/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 1.0, lr: 6.370e-05, size: 576, ETA: 0:22:31
2025-08-28 16:01:05.276 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 532/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.8, lr: 6.341e-05, size: 288, ETA: 0:22:28
2025-08-28 16:01:08.413 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 532/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 6.313e-05, size: 352, ETA: 0:22:25
2025-08-28 16:01:11.469 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 532/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.5, lr: 6.285e-05, size: 576, ETA: 0:22:22
2025-08-28 16:01:12.864 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:01:19.329 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:01:20.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:01:20.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6106
2025-08-28 16:01:20.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5067
2025-08-28 16:01:20.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4105
2025-08-28 16:01:20.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5093
2025-08-28 16:01:20.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:01:20.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:01:20.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.611
2025-08-28 16:01:20.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-08-28 16:01:20.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-08-28 16:01:20.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.509
2025-08-28 16:01:20.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:01:20.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:01:20.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:01:20.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:01:20.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:01:20.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:01:20.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:01:20.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:01:20.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:01:21.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:01:22.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:01:23.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:01:23.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:01:24.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:01:25.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:01:25.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:01:26.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:01:27.168 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:01:27.168 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:01:27.168 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:01:27.168 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:01:27.175 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.93 ms, Average inference time: 7.06 ms

2025-08-28 16:01:27.176 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:01:27.257 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:01:27.335 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch533
2025-08-28 16:01:30.147 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 533/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.138s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 6.244e-05, size: 256, ETA: 0:22:17
2025-08-28 16:01:33.253 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 533/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.6, lr: 6.215e-05, size: 544, ETA: 0:22:14
2025-08-28 16:01:36.235 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 533/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 6.187e-05, size: 448, ETA: 0:22:11
2025-08-28 16:01:39.180 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 533/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 6.159e-05, size: 352, ETA: 0:22:08
2025-08-28 16:01:42.237 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 533/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 1.0, lr: 6.131e-05, size: 256, ETA: 0:22:05
2025-08-28 16:01:45.194 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 533/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 6.103e-05, size: 512, ETA: 0:22:02
2025-08-28 16:01:46.534 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:01:52.514 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:01:53.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:01:54.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5956
2025-08-28 16:01:54.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5276
2025-08-28 16:01:54.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3801
2025-08-28 16:01:54.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5011
2025-08-28 16:01:54.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:01:54.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:01:54.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.596
2025-08-28 16:01:54.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-08-28 16:01:54.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-08-28 16:01:54.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-08-28 16:01:54.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:01:54.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:01:54.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:01:54.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:01:54.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:01:54.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:01:54.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:01:54.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:01:54.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:01:55.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:01:56.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:01:58.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:01:59.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:02:00.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:02:01.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:02:02.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:02:03.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:02:04.356 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:02:04.356 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:02:04.356 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 16:02:04.356 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:02:04.363 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.92 ms, Average inference time: 7.14 ms

2025-08-28 16:02:04.364 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:02:04.453 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:02:04.533 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch534
2025-08-28 16:02:07.422 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 534/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.9, lr: 6.063e-05, size: 576, ETA: 0:21:57
2025-08-28 16:02:10.727 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 534/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 1.2, conf_loss: 1.5, cls_loss: 0.6, lr: 6.035e-05, size: 544, ETA: 0:21:54
2025-08-28 16:02:13.737 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 534/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 6.007e-05, size: 320, ETA: 0:21:51
2025-08-28 16:02:16.824 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 534/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 5.979e-05, size: 576, ETA: 0:21:48
2025-08-28 16:02:19.954 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 534/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 3.6, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 1.2, cls_loss: 0.5, lr: 5.952e-05, size: 512, ETA: 0:21:45
2025-08-28 16:02:22.961 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 534/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.7, lr: 5.924e-05, size: 288, ETA: 0:21:42
2025-08-28 16:02:24.270 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:02:30.488 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:02:31.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:02:31.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5992
2025-08-28 16:02:32.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5017
2025-08-28 16:02:32.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3959
2025-08-28 16:02:32.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4989
2025-08-28 16:02:32.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:02:32.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:02:32.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-08-28 16:02:32.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-08-28 16:02:32.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-08-28 16:02:32.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.499
2025-08-28 16:02:32.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:02:32.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:02:32.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:02:32.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:02:32.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:02:32.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:02:32.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:02:32.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:02:32.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:02:32.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:02:33.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:02:34.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:02:35.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:02:35.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:02:36.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:02:37.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:02:38.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:02:38.818 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:02:38.818 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:02:38.818 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 16:02:38.819 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:02:38.825 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.89 ms, Average inference time: 7.15 ms

2025-08-28 16:02:38.826 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:02:38.910 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:02:38.988 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch535
2025-08-28 16:02:41.927 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 535/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 5.884e-05, size: 512, ETA: 0:21:38
2025-08-28 16:02:44.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 535/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 5.857e-05, size: 320, ETA: 0:21:35
2025-08-28 16:02:47.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 535/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 5.830e-05, size: 256, ETA: 0:21:32
2025-08-28 16:02:50.982 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 535/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 5.802e-05, size: 288, ETA: 0:21:29
2025-08-28 16:02:53.989 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 535/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 1.0, lr: 5.775e-05, size: 288, ETA: 0:21:25
2025-08-28 16:02:57.092 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 535/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 5.748e-05, size: 416, ETA: 0:21:22
2025-08-28 16:02:58.485 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:03:04.603 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:03:05.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:03:05.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5714
2025-08-28 16:03:05.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5128
2025-08-28 16:03:05.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3614
2025-08-28 16:03:05.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4818
2025-08-28 16:03:05.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:03:05.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:03:05.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.571
2025-08-28 16:03:05.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-08-28 16:03:05.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-08-28 16:03:05.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.482
2025-08-28 16:03:05.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:03:05.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:03:05.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:03:05.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:03:05.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:03:05.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:03:05.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:03:05.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:03:05.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:03:06.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:03:06.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:03:07.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:03:07.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:03:08.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:03:08.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:03:09.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:03:09.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:03:10.100 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:03:10.101 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 16:03:10.101 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 16:03:10.101 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:03:10.113 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.91 ms, Average inference time: 7.13 ms

2025-08-28 16:03:10.114 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:03:10.235 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:03:10.347 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch536
2025-08-28 16:03:13.219 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 536/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.7, lr: 5.709e-05, size: 384, ETA: 0:21:18
2025-08-28 16:03:16.225 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 536/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 5.682e-05, size: 576, ETA: 0:21:15
2025-08-28 16:03:19.360 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 536/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 5.655e-05, size: 576, ETA: 0:21:12
2025-08-28 16:03:22.481 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 536/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.005s, total_loss: 3.6, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 1.0, cls_loss: 0.5, lr: 5.628e-05, size: 352, ETA: 0:21:09
2025-08-28 16:03:25.567 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 536/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 5.601e-05, size: 256, ETA: 0:21:06
2025-08-28 16:03:28.556 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 536/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 1.0, lr: 5.574e-05, size: 416, ETA: 0:21:03
2025-08-28 16:03:29.901 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:03:36.148 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:03:37.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:03:37.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6095
2025-08-28 16:03:37.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5232
2025-08-28 16:03:37.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3996
2025-08-28 16:03:37.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5107
2025-08-28 16:03:37.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:03:37.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:03:37.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.610
2025-08-28 16:03:37.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-08-28 16:03:37.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-28 16:03:37.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-08-28 16:03:37.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:03:37.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:03:37.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:03:37.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:03:37.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:03:37.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:03:37.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:03:37.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:03:37.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:03:38.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:03:39.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:03:39.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:03:40.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:03:41.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:03:42.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:03:42.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:03:43.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:03:44.451 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:03:44.452 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:03:44.452 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:03:44.452 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:03:44.459 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.92 ms, Average inference time: 7.17 ms

2025-08-28 16:03:44.460 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:03:44.536 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:03:44.668 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch537
2025-08-28 16:03:47.656 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 537/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 3.1, cls_loss: 0.7, lr: 5.536e-05, size: 416, ETA: 0:20:58
2025-08-28 16:03:50.628 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 537/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 5.509e-05, size: 544, ETA: 0:20:55
2025-08-28 16:03:53.795 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 537/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 5.483e-05, size: 384, ETA: 0:20:52
2025-08-28 16:03:56.961 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 537/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 0.9, cls_loss: 0.6, lr: 5.456e-05, size: 320, ETA: 0:20:49
2025-08-28 16:03:59.916 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 537/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 5.430e-05, size: 320, ETA: 0:20:46
2025-08-28 16:04:02.952 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 537/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.7, lr: 5.403e-05, size: 320, ETA: 0:20:43
2025-08-28 16:04:04.312 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:04:10.782 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:04:11.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:04:12.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5927
2025-08-28 16:04:12.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4974
2025-08-28 16:04:12.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3609
2025-08-28 16:04:12.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4837
2025-08-28 16:04:12.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:04:12.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:04:12.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-08-28 16:04:12.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-08-28 16:04:12.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-08-28 16:04:12.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-08-28 16:04:12.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:04:12.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:04:12.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:04:12.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:04:12.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:04:12.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:04:12.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:04:12.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:04:12.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:04:13.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:04:13.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:04:14.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:04:15.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:04:16.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:04:16.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:04:17.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:04:18.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:04:19.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:04:19.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 16:04:19.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-08-28 16:04:19.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:04:19.204 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.93 ms, Average inference time: 7.08 ms

2025-08-28 16:04:19.205 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:04:19.286 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:04:19.371 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch538
2025-08-28 16:04:22.182 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 538/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 3.1, cls_loss: 0.7, lr: 5.365e-05, size: 256, ETA: 0:20:38
2025-08-28 16:04:25.251 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 538/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 5.339e-05, size: 448, ETA: 0:20:35
2025-08-28 16:04:28.378 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 538/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 5.313e-05, size: 512, ETA: 0:20:32
2025-08-28 16:04:31.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 538/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.6, lr: 5.287e-05, size: 544, ETA: 0:20:29
2025-08-28 16:04:34.507 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 538/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 0.8, cls_loss: 0.5, lr: 5.261e-05, size: 448, ETA: 0:20:26
2025-08-28 16:04:37.669 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 538/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.5, lr: 5.235e-05, size: 384, ETA: 0:20:23
2025-08-28 16:04:39.009 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:04:45.266 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:04:46.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:04:46.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6061
2025-08-28 16:04:46.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5225
2025-08-28 16:04:46.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3944
2025-08-28 16:04:46.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5077
2025-08-28 16:04:46.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:04:46.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:04:46.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.606
2025-08-28 16:04:46.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-08-28 16:04:46.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-08-28 16:04:46.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.508
2025-08-28 16:04:46.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:04:46.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:04:46.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:04:46.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:04:46.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:04:46.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:04:46.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:04:46.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:04:46.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:04:47.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:04:48.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:04:49.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:04:49.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:04:50.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:04:51.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:04:51.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:04:52.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:04:53.202 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:04:53.202 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 16:04:53.202 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:04:53.203 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:04:53.216 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.93 ms, Average inference time: 7.09 ms

2025-08-28 16:04:53.230 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:04:53.341 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:04:53.415 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch539
2025-08-28 16:04:56.378 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 539/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 5.197e-05, size: 384, ETA: 0:20:19
2025-08-28 16:04:59.339 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 539/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 5.171e-05, size: 352, ETA: 0:20:16
2025-08-28 16:05:02.461 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 539/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 5.146e-05, size: 480, ETA: 0:20:13
2025-08-28 16:05:05.476 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 539/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 5.120e-05, size: 576, ETA: 0:20:09
2025-08-28 16:05:08.690 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 539/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 5.094e-05, size: 544, ETA: 0:20:06
2025-08-28 16:05:11.747 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 539/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 5.069e-05, size: 352, ETA: 0:20:03
2025-08-28 16:05:13.082 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:05:19.284 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:05:20.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:05:20.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5888
2025-08-28 16:05:20.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5382
2025-08-28 16:05:20.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4144
2025-08-28 16:05:20.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5138
2025-08-28 16:05:20.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:05:20.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:05:20.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-08-28 16:05:20.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.538
2025-08-28 16:05:20.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-08-28 16:05:20.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-08-28 16:05:20.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:05:20.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:05:20.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:05:20.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:05:20.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:05:20.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:05:20.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:05:20.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:05:20.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:05:21.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:05:22.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:05:22.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:05:23.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:05:24.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:05:25.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:05:25.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:05:26.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:05:27.155 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:05:27.155 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 16:05:27.155 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:05:27.155 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:05:27.162 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.91 ms, Average inference time: 7.07 ms

2025-08-28 16:05:27.164 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:05:27.251 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:05:27.329 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch540
2025-08-28 16:05:30.322 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 540/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 5.032e-05, size: 352, ETA: 0:19:59
2025-08-28 16:05:33.439 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 540/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 5.007e-05, size: 512, ETA: 0:19:56
2025-08-28 16:05:36.409 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 540/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 4.981e-05, size: 512, ETA: 0:19:53
2025-08-28 16:05:39.404 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 540/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.8, lr: 4.956e-05, size: 448, ETA: 0:19:50
2025-08-28 16:05:42.444 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 540/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.6, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.5, lr: 4.931e-05, size: 576, ETA: 0:19:47
2025-08-28 16:05:45.466 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 540/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.5, lr: 4.906e-05, size: 512, ETA: 0:19:44
2025-08-28 16:05:46.832 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:05:52.869 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:05:53.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:05:53.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6053
2025-08-28 16:05:53.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5254
2025-08-28 16:05:54.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4285
2025-08-28 16:05:54.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5197
2025-08-28 16:05:54.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:05:54.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:05:54.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.605
2025-08-28 16:05:54.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 16:05:54.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-08-28 16:05:54.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.520
2025-08-28 16:05:54.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:05:54.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:05:54.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:05:54.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:05:54.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:05:54.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:05:54.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:05:54.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:05:54.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:05:54.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:05:55.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:05:55.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:05:56.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:05:56.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:05:57.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:05:57.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:05:58.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:05:58.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:05:58.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:05:58.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:05:58.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:05:58.520 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.88 ms, Average inference time: 7.05 ms

2025-08-28 16:05:58.521 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:05:58.598 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:05:58.678 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch541
2025-08-28 16:06:01.445 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 541/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.137s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 4.869e-05, size: 320, ETA: 0:19:39
2025-08-28 16:06:04.560 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 541/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 4.844e-05, size: 448, ETA: 0:19:36
2025-08-28 16:06:07.710 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 541/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 4.819e-05, size: 384, ETA: 0:19:33
2025-08-28 16:06:10.640 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 541/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 4.795e-05, size: 352, ETA: 0:19:30
2025-08-28 16:06:13.599 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 541/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 4.770e-05, size: 416, ETA: 0:19:27
2025-08-28 16:06:16.716 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 541/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.8, lr: 4.745e-05, size: 256, ETA: 0:19:24
2025-08-28 16:06:18.177 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:06:24.331 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:06:24.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:06:25.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5801
2025-08-28 16:06:25.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5115
2025-08-28 16:06:25.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3948
2025-08-28 16:06:25.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4955
2025-08-28 16:06:25.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:06:25.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:06:25.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.580
2025-08-28 16:06:25.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-08-28 16:06:25.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-08-28 16:06:25.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-08-28 16:06:25.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:06:25.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:06:25.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:06:25.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:06:25.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:06:25.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:06:25.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:06:25.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:06:25.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:06:25.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:06:26.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:06:26.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:06:27.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:06:27.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:06:27.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:06:28.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:06:28.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:06:29.128 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:06:29.128 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:06:29.128 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 16:06:29.128 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:06:29.135 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.34 ms, Average NMS time: 0.86 ms, Average inference time: 7.20 ms

2025-08-28 16:06:29.135 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:06:29.269 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:06:29.339 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch542
2025-08-28 16:06:32.181 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 542/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 1.0, lr: 4.709e-05, size: 480, ETA: 0:19:19
2025-08-28 16:06:35.260 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 542/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.7, lr: 4.685e-05, size: 448, ETA: 0:19:16
2025-08-28 16:06:38.417 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 542/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.5, l1_loss: 0.7, conf_loss: 0.8, cls_loss: 0.5, lr: 4.660e-05, size: 544, ETA: 0:19:13
2025-08-28 16:06:41.399 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 542/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 4.636e-05, size: 480, ETA: 0:19:10
2025-08-28 16:06:44.396 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 542/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.7, lr: 4.611e-05, size: 288, ETA: 0:19:07
2025-08-28 16:06:47.338 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 542/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.8, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.7, lr: 4.587e-05, size: 288, ETA: 0:19:04
2025-08-28 16:06:48.639 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:06:54.902 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:06:55.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:06:56.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6116
2025-08-28 16:06:56.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5383
2025-08-28 16:06:56.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4241
2025-08-28 16:06:56.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5247
2025-08-28 16:06:56.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:06:56.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:06:56.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.612
2025-08-28 16:06:56.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.538
2025-08-28 16:06:56.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-08-28 16:06:56.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.525
2025-08-28 16:06:56.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:06:56.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:06:56.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:06:56.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:06:56.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:06:56.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:06:56.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:06:56.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:06:56.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:06:57.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:06:58.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:06:59.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:07:00.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:07:01.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:07:02.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:07:03.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:07:04.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:07:05.007 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:07:05.007 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:07:05.008 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:07:05.008 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:07:05.022 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.95 ms, Average inference time: 7.24 ms

2025-08-28 16:07:05.023 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:07:05.105 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:07:05.190 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch543
2025-08-28 16:07:07.976 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 543/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.138s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 4.552e-05, size: 384, ETA: 0:19:00
2025-08-28 16:07:10.922 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 543/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 1.1, lr: 4.528e-05, size: 256, ETA: 0:18:56
2025-08-28 16:07:14.057 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 543/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 4.503e-05, size: 416, ETA: 0:18:53
2025-08-28 16:07:16.973 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 543/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.8, lr: 4.479e-05, size: 384, ETA: 0:18:50
2025-08-28 16:07:19.910 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 543/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 4.455e-05, size: 512, ETA: 0:18:47
2025-08-28 16:07:22.912 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 543/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 4.431e-05, size: 352, ETA: 0:18:44
2025-08-28 16:07:24.206 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:07:30.288 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:07:31.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:07:32.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5945
2025-08-28 16:07:32.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5248
2025-08-28 16:07:32.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3520
2025-08-28 16:07:32.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4904
2025-08-28 16:07:32.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:07:32.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:07:32.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.594
2025-08-28 16:07:32.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 16:07:32.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.352
2025-08-28 16:07:32.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-08-28 16:07:32.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:07:32.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:07:32.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:07:32.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:07:32.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:07:32.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:07:32.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:07:32.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:07:32.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:07:33.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:07:35.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:07:36.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:07:37.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:07:38.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:07:39.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:07:40.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:07:41.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:07:42.909 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:07:42.909 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 16:07:42.909 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 16:07:42.909 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:07:42.917 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.93 ms, Average inference time: 7.11 ms

2025-08-28 16:07:42.918 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:07:43.062 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:07:43.175 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch544
2025-08-28 16:07:46.041 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 544/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.8, lr: 4.397e-05, size: 320, ETA: 0:18:40
2025-08-28 16:07:49.150 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 544/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.8, lr: 4.373e-05, size: 416, ETA: 0:18:37
2025-08-28 16:07:52.127 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 544/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 4.349e-05, size: 256, ETA: 0:18:34
2025-08-28 16:07:55.228 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 544/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 4.326e-05, size: 576, ETA: 0:18:31
2025-08-28 16:07:58.403 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 544/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.9, lr: 4.302e-05, size: 544, ETA: 0:18:28
2025-08-28 16:08:01.395 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 544/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.6, lr: 4.279e-05, size: 320, ETA: 0:18:24
2025-08-28 16:08:02.752 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:08:09.012 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:08:09.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:08:10.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6068
2025-08-28 16:08:10.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5330
2025-08-28 16:08:10.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4215
2025-08-28 16:08:10.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5204
2025-08-28 16:08:10.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:08:10.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:08:10.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-08-28 16:08:10.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-08-28 16:08:10.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.421
2025-08-28 16:08:10.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.520
2025-08-28 16:08:10.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:08:10.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:08:10.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:08:10.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:08:10.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:08:10.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:08:10.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:08:10.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:08:10.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:08:11.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:08:11.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:08:12.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:08:13.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:08:13.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:08:14.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:08:14.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:08:15.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:08:16.232 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:08:16.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:08:16.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:08:16.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:08:16.240 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.88 ms, Average inference time: 7.09 ms

2025-08-28 16:08:16.241 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:08:16.325 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:08:16.445 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch545
2025-08-28 16:08:19.423 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 545/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.1, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 1.2, lr: 4.245e-05, size: 512, ETA: 0:18:20
2025-08-28 16:08:22.379 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 545/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 4.221e-05, size: 384, ETA: 0:18:17
2025-08-28 16:08:25.359 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 545/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 4.198e-05, size: 512, ETA: 0:18:14
2025-08-28 16:08:28.403 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 545/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 4.175e-05, size: 448, ETA: 0:18:11
2025-08-28 16:08:31.392 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 545/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 4.152e-05, size: 384, ETA: 0:18:08
2025-08-28 16:08:34.312 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 545/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 4.128e-05, size: 320, ETA: 0:18:05
2025-08-28 16:08:35.678 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:08:41.806 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:08:42.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:08:43.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5950
2025-08-28 16:08:43.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5214
2025-08-28 16:08:43.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3928
2025-08-28 16:08:43.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5031
2025-08-28 16:08:43.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:08:43.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:08:43.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.595
2025-08-28 16:08:43.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-08-28 16:08:43.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-08-28 16:08:43.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-08-28 16:08:43.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:08:43.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:08:43.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:08:43.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:08:43.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:08:43.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:08:43.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:08:43.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:08:43.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:08:43.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:08:44.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:08:45.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:08:45.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:08:46.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:08:46.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:08:47.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:08:48.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:08:48.697 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:08:48.698 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 16:08:48.698 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 16:08:48.698 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:08:48.705 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.90 ms, Average inference time: 7.12 ms

2025-08-28 16:08:48.706 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:08:48.831 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:08:48.903 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch546
2025-08-28 16:08:51.755 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 546/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 4.095e-05, size: 576, ETA: 0:18:00
2025-08-28 16:08:54.935 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 546/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 10.6, iou_loss: 3.5, l1_loss: 1.8, conf_loss: 4.2, cls_loss: 1.1, lr: 4.072e-05, size: 512, ETA: 0:17:57
2025-08-28 16:08:58.001 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 546/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 4.049e-05, size: 352, ETA: 0:17:54
2025-08-28 16:09:01.025 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 546/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.9, lr: 4.026e-05, size: 576, ETA: 0:17:51
2025-08-28 16:09:04.179 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 546/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 4.004e-05, size: 576, ETA: 0:17:48
2025-08-28 16:09:07.307 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 546/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.2, cls_loss: 0.7, lr: 3.981e-05, size: 480, ETA: 0:17:45
2025-08-28 16:09:08.663 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:09:14.767 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:09:15.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:09:16.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6004
2025-08-28 16:09:16.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5358
2025-08-28 16:09:16.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3802
2025-08-28 16:09:16.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5055
2025-08-28 16:09:16.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:09:16.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:09:16.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-08-28 16:09:16.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-08-28 16:09:16.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-08-28 16:09:16.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-08-28 16:09:16.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:09:16.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:09:16.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:09:16.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:09:16.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:09:16.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:09:16.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:09:16.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:09:16.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:09:16.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:09:17.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:09:18.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:09:18.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:09:19.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:09:20.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:09:20.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:09:21.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:09:22.271 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:09:22.271 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:09:22.272 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:09:22.272 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:09:22.279 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.92 ms, Average inference time: 7.05 ms

2025-08-28 16:09:22.280 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:09:22.363 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:09:22.443 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch547
2025-08-28 16:09:25.377 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 547/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.7, lr: 3.948e-05, size: 576, ETA: 0:17:41
2025-08-28 16:09:28.486 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 547/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.6, lr: 3.925e-05, size: 320, ETA: 0:17:37
2025-08-28 16:09:31.405 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 547/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.9, lr: 3.903e-05, size: 256, ETA: 0:17:34
2025-08-28 16:09:34.351 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 547/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 3.2, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 1.0, cls_loss: 0.5, lr: 3.881e-05, size: 480, ETA: 0:17:31
2025-08-28 16:09:37.312 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 547/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.8, lr: 3.858e-05, size: 448, ETA: 0:17:28
2025-08-28 16:09:40.356 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 547/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 3.836e-05, size: 352, ETA: 0:17:25
2025-08-28 16:09:41.707 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:09:47.978 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:09:48.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:09:48.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5755
2025-08-28 16:09:48.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4965
2025-08-28 16:09:48.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3882
2025-08-28 16:09:48.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4867
2025-08-28 16:09:48.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:09:48.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:09:48.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.575
2025-08-28 16:09:48.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-08-28 16:09:48.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-08-28 16:09:48.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-08-28 16:09:48.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:09:48.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:09:48.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:09:48.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:09:48.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:09:48.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:09:48.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:09:48.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:09:48.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:09:49.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:09:49.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:09:49.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:09:50.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:09:50.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:09:50.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:09:51.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:09:51.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:09:51.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:09:51.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 16:09:51.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 16:09:51.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:09:51.899 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.82 ms, Average inference time: 7.01 ms

2025-08-28 16:09:51.900 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:09:51.986 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:09:52.066 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch548
2025-08-28 16:09:55.081 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 548/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.4, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 2.4, cls_loss: 0.6, lr: 3.804e-05, size: 256, ETA: 0:17:21
2025-08-28 16:09:58.044 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 548/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.7, lr: 3.782e-05, size: 320, ETA: 0:17:18
2025-08-28 16:10:01.121 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 548/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 3.759e-05, size: 512, ETA: 0:17:15
2025-08-28 16:10:04.125 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 548/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.6, lr: 3.737e-05, size: 480, ETA: 0:17:12
2025-08-28 16:10:07.156 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 548/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 3.715e-05, size: 352, ETA: 0:17:08
2025-08-28 16:10:10.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 548/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 3.694e-05, size: 256, ETA: 0:17:05
2025-08-28 16:10:11.560 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:10:17.740 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:10:18.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:10:19.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6089
2025-08-28 16:10:19.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5375
2025-08-28 16:10:19.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4127
2025-08-28 16:10:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5197
2025-08-28 16:10:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:10:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:10:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-08-28 16:10:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-08-28 16:10:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-28 16:10:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.520
2025-08-28 16:10:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:10:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:10:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:10:19.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:10:19.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:10:19.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:10:19.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:10:19.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:10:19.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:10:20.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:10:20.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:10:21.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:10:22.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:10:23.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:10:24.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:10:24.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:10:25.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:10:26.357 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:10:26.357 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:10:26.357 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:10:26.357 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:10:26.364 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.92 ms, Average inference time: 7.15 ms

2025-08-28 16:10:26.366 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:10:26.443 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:10:26.524 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch549
2025-08-28 16:10:29.378 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 549/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 3.662e-05, size: 544, ETA: 0:17:01
2025-08-28 16:10:32.421 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 549/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 3.640e-05, size: 288, ETA: 0:16:58
2025-08-28 16:10:35.368 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 549/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 3.3, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 0.7, cls_loss: 0.5, lr: 3.619e-05, size: 576, ETA: 0:16:55
2025-08-28 16:10:38.548 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 549/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 3.597e-05, size: 576, ETA: 0:16:52
2025-08-28 16:10:41.656 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 549/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.8, lr: 3.575e-05, size: 352, ETA: 0:16:49
2025-08-28 16:10:44.720 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 549/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.7, lr: 3.554e-05, size: 384, ETA: 0:16:46
2025-08-28 16:10:46.105 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:10:52.121 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:10:53.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:10:53.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5886
2025-08-28 16:10:53.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4897
2025-08-28 16:10:53.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3995
2025-08-28 16:10:53.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4926
2025-08-28 16:10:53.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:10:53.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:10:53.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-08-28 16:10:53.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-08-28 16:10:53.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-28 16:10:53.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-08-28 16:10:53.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:10:53.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:10:53.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:10:53.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:10:53.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:10:53.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:10:53.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:10:53.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:10:53.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:10:54.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:10:55.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:10:56.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:10:57.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:10:57.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:10:58.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:10:59.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:11:00.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:11:01.133 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:11:01.133 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 16:11:01.133 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 16:11:01.133 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:11:01.140 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.08 ms, Average NMS time: 0.91 ms, Average inference time: 7.00 ms

2025-08-28 16:11:01.141 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:11:01.230 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:11:01.327 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch550
2025-08-28 16:11:04.164 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 550/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 3.523e-05, size: 256, ETA: 0:16:41
2025-08-28 16:11:07.193 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 550/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 3.502e-05, size: 384, ETA: 0:16:38
2025-08-28 16:11:10.191 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 550/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.6, lr: 3.480e-05, size: 576, ETA: 0:16:35
2025-08-28 16:11:13.245 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 550/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 3.459e-05, size: 480, ETA: 0:16:32
2025-08-28 16:11:16.298 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 550/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 3.438e-05, size: 480, ETA: 0:16:29
2025-08-28 16:11:19.339 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 550/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 3.417e-05, size: 352, ETA: 0:16:26
2025-08-28 16:11:20.669 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:11:26.805 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:11:27.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:11:28.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6046
2025-08-28 16:11:28.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5415
2025-08-28 16:11:28.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4107
2025-08-28 16:11:28.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5189
2025-08-28 16:11:28.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:11:28.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:11:28.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.605
2025-08-28 16:11:28.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-08-28 16:11:28.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-08-28 16:11:28.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.519
2025-08-28 16:11:28.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:11:28.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:11:28.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:11:28.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:11:28.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:11:28.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:11:28.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:11:28.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:11:28.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:11:29.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:11:29.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:11:30.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:11:31.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:11:32.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:11:32.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:11:33.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:11:34.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:11:34.922 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:11:34.922 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.28
2025-08-28 16:11:34.922 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:11:34.922 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:11:34.929 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.91 ms, Average inference time: 7.02 ms

2025-08-28 16:11:34.930 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:11:35.036 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:11:35.113 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch551
2025-08-28 16:11:38.008 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 551/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 0.9, cls_loss: 0.6, lr: 3.386e-05, size: 384, ETA: 0:16:21
2025-08-28 16:11:41.059 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 551/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 3.366e-05, size: 448, ETA: 0:16:18
2025-08-28 16:11:44.298 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 551/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 1.4, conf_loss: 1.8, cls_loss: 0.7, lr: 3.345e-05, size: 576, ETA: 0:16:15
2025-08-28 16:11:47.402 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 551/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 3.324e-05, size: 352, ETA: 0:16:12
2025-08-28 16:11:50.367 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 551/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 2.1, cls_loss: 0.6, lr: 3.303e-05, size: 512, ETA: 0:16:09
2025-08-28 16:11:53.399 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 551/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.5, lr: 3.283e-05, size: 480, ETA: 0:16:06
2025-08-28 16:11:54.779 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:12:01.055 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:12:01.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:12:02.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6050
2025-08-28 16:12:02.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5223
2025-08-28 16:12:02.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3834
2025-08-28 16:12:02.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5036
2025-08-28 16:12:02.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:12:02.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:12:02.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.605
2025-08-28 16:12:02.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-08-28 16:12:02.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-28 16:12:02.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-08-28 16:12:02.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:12:02.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:12:02.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:12:02.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:12:02.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:12:02.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:12:02.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:12:02.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:12:02.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:12:03.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:12:03.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:12:04.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:12:04.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:12:05.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:12:06.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:12:06.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:12:07.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:12:07.911 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:12:07.912 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:12:07.912 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 16:12:07.912 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:12:07.919 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.33 ms, Average NMS time: 0.88 ms, Average inference time: 7.22 ms

2025-08-28 16:12:07.920 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:12:08.007 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:12:08.088 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch552
2025-08-28 16:12:11.162 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 552/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 3.253e-05, size: 544, ETA: 0:16:02
2025-08-28 16:12:14.405 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 552/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 3.3, cls_loss: 0.8, lr: 3.232e-05, size: 480, ETA: 0:15:59
2025-08-28 16:12:17.444 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 552/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 3.212e-05, size: 480, ETA: 0:15:56
2025-08-28 16:12:20.601 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 552/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.6, lr: 3.191e-05, size: 544, ETA: 0:15:53
2025-08-28 16:12:23.679 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 552/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.8, lr: 3.171e-05, size: 384, ETA: 0:15:50
2025-08-28 16:12:26.683 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 552/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 3.151e-05, size: 352, ETA: 0:15:46
2025-08-28 16:12:28.052 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:12:34.146 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:12:34.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:12:35.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6078
2025-08-28 16:12:35.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5276
2025-08-28 16:12:35.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3952
2025-08-28 16:12:35.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5102
2025-08-28 16:12:35.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:12:35.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:12:35.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-08-28 16:12:35.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-08-28 16:12:35.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-08-28 16:12:35.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.510
2025-08-28 16:12:35.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:12:35.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:12:35.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:12:35.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:12:35.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:12:35.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:12:35.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:12:35.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:12:35.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:12:36.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:12:36.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:12:37.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:12:37.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:12:38.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:12:39.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:12:39.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:12:40.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:12:40.736 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:12:40.736 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:12:40.736 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:12:40.736 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:12:40.743 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.94 ms, Average inference time: 7.08 ms

2025-08-28 16:12:40.745 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:12:40.880 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:12:40.951 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch553
2025-08-28 16:12:43.902 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 553/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 3.122e-05, size: 544, ETA: 0:15:42
2025-08-28 16:12:47.024 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 553/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 3.101e-05, size: 384, ETA: 0:15:39
2025-08-28 16:12:50.092 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 553/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.9, lr: 3.081e-05, size: 288, ETA: 0:15:36
2025-08-28 16:12:53.298 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 553/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.6, lr: 3.061e-05, size: 448, ETA: 0:15:33
2025-08-28 16:12:56.271 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 553/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.7, lr: 3.042e-05, size: 416, ETA: 0:15:30
2025-08-28 16:12:59.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 553/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 3.022e-05, size: 384, ETA: 0:15:27
2025-08-28 16:13:00.565 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:13:06.671 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:13:07.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:13:08.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5799
2025-08-28 16:13:08.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4999
2025-08-28 16:13:08.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3050
2025-08-28 16:13:08.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4616
2025-08-28 16:13:08.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:13:08.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:13:08.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.580
2025-08-28 16:13:08.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-08-28 16:13:08.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.305
2025-08-28 16:13:08.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.462
2025-08-28 16:13:08.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:13:08.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:13:08.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:13:08.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:13:08.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:13:08.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:13:08.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:13:08.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:13:08.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:13:09.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:13:09.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:13:10.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:13:11.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:13:11.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:13:12.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:13:13.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:13:14.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:13:14.803 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:13:14.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-08-28 16:13:14.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-08-28 16:13:14.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:13:14.811 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.91 ms, Average inference time: 7.12 ms

2025-08-28 16:13:14.812 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:13:14.899 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:13:14.981 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch554
2025-08-28 16:13:17.786 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 554/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.138s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.7, lr: 2.993e-05, size: 256, ETA: 0:15:22
2025-08-28 16:13:20.832 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 554/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 2.973e-05, size: 416, ETA: 0:15:19
2025-08-28 16:13:23.909 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 554/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.005s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.7, lr: 2.954e-05, size: 384, ETA: 0:15:16
2025-08-28 16:13:26.841 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 554/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 2.934e-05, size: 416, ETA: 0:15:13
2025-08-28 16:13:29.846 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 554/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 2.915e-05, size: 352, ETA: 0:15:10
2025-08-28 16:13:32.774 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 554/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.9, lr: 2.895e-05, size: 544, ETA: 0:15:07
2025-08-28 16:13:34.236 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:13:40.325 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:13:41.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:13:41.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6085
2025-08-28 16:13:41.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5195
2025-08-28 16:13:41.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4127
2025-08-28 16:13:41.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5136
2025-08-28 16:13:41.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:13:41.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:13:41.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-08-28 16:13:41.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-08-28 16:13:41.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-28 16:13:41.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-08-28 16:13:41.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:13:41.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:13:41.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:13:41.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:13:41.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:13:41.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:13:41.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:13:41.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:13:41.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:13:42.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:13:42.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:13:43.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:13:44.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:13:44.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:13:45.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:13:46.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:13:46.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:13:47.259 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:13:47.259 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:13:47.259 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:13:47.259 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:13:47.272 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.93 ms, Average inference time: 7.02 ms

2025-08-28 16:13:47.273 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:13:47.388 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:13:47.475 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch555
2025-08-28 16:13:50.463 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 555/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.8, lr: 2.867e-05, size: 512, ETA: 0:15:03
2025-08-28 16:13:53.428 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 555/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 2.848e-05, size: 352, ETA: 0:14:59
2025-08-28 16:13:56.606 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 555/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 2.829e-05, size: 544, ETA: 0:14:56
2025-08-28 16:13:59.646 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 555/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.1, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.4, lr: 2.810e-05, size: 448, ETA: 0:14:53
2025-08-28 16:14:02.739 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 555/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.2, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.6, lr: 2.791e-05, size: 256, ETA: 0:14:50
2025-08-28 16:14:05.935 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 555/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 2.772e-05, size: 512, ETA: 0:14:47
2025-08-28 16:14:07.333 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:14:13.475 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:14:14.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:14:14.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5961
2025-08-28 16:14:14.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5286
2025-08-28 16:14:14.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3991
2025-08-28 16:14:14.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5079
2025-08-28 16:14:14.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:14:14.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:14:14.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.596
2025-08-28 16:14:14.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-08-28 16:14:14.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-08-28 16:14:14.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.508
2025-08-28 16:14:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:14:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:14:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:14:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:14:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:14:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:14:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:14:14.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:14:14.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:14:15.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:14:16.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:14:16.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:14:17.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:14:18.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:14:18.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:14:19.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:14:19.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:14:20.411 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:14:20.411 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:14:20.411 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:14:20.412 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:14:20.423 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.88 ms, Average inference time: 7.06 ms

2025-08-28 16:14:20.424 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:14:20.544 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:14:20.657 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch556
2025-08-28 16:14:23.532 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 556/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 2.744e-05, size: 384, ETA: 0:14:43
2025-08-28 16:14:26.555 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 556/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 2.725e-05, size: 512, ETA: 0:14:40
2025-08-28 16:14:29.691 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 556/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.006s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 2.706e-05, size: 544, ETA: 0:14:37
2025-08-28 16:14:32.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 556/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.7, lr: 2.688e-05, size: 416, ETA: 0:14:34
2025-08-28 16:14:35.625 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 556/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 2.669e-05, size: 512, ETA: 0:14:31
2025-08-28 16:14:38.693 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 556/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 3.5, cls_loss: 0.8, lr: 2.650e-05, size: 544, ETA: 0:14:28
2025-08-28 16:14:40.060 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:14:46.204 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:14:46.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:14:47.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6034
2025-08-28 16:14:47.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5016
2025-08-28 16:14:47.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4102
2025-08-28 16:14:47.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5051
2025-08-28 16:14:47.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:14:47.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:14:47.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.603
2025-08-28 16:14:47.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-08-28 16:14:47.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-08-28 16:14:47.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-08-28 16:14:47.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:14:47.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:14:47.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:14:47.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:14:47.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:14:47.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:14:47.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:14:47.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:14:47.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:14:47.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:14:48.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:14:48.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:14:49.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:14:49.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:14:50.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:14:50.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:14:51.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:14:52.014 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:14:52.014 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:14:52.014 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:14:52.014 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:14:52.021 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.89 ms, Average inference time: 7.02 ms

2025-08-28 16:14:52.022 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:14:52.103 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:14:52.181 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch557
2025-08-28 16:14:55.158 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 557/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 2.624e-05, size: 544, ETA: 0:14:23
2025-08-28 16:14:58.199 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 557/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.6, lr: 2.605e-05, size: 480, ETA: 0:14:20
2025-08-28 16:15:01.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 557/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 8.1, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.7, lr: 2.587e-05, size: 512, ETA: 0:14:17
2025-08-28 16:15:04.294 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 557/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 2.568e-05, size: 384, ETA: 0:14:14
2025-08-28 16:15:07.272 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 557/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 2.550e-05, size: 384, ETA: 0:14:11
2025-08-28 16:15:10.376 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 557/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 2.532e-05, size: 544, ETA: 0:14:08
2025-08-28 16:15:11.763 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:15:18.006 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:15:18.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:15:19.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6003
2025-08-28 16:15:19.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5303
2025-08-28 16:15:19.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4000
2025-08-28 16:15:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5102
2025-08-28 16:15:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:15:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:15:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-08-28 16:15:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 16:15:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-28 16:15:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.510
2025-08-28 16:15:19.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:15:19.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:15:19.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:15:19.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:15:19.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:15:19.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:15:19.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:15:19.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:15:19.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:15:19.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:15:20.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:15:21.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:15:21.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:15:22.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:15:22.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:15:23.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:15:23.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:15:24.501 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:15:24.501 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:15:24.502 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:15:24.502 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:15:24.514 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.89 ms, Average inference time: 7.18 ms

2025-08-28 16:15:24.515 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:15:24.628 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:15:24.704 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch558
2025-08-28 16:15:27.622 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 558/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 9.0, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 4.3, cls_loss: 0.8, lr: 2.506e-05, size: 544, ETA: 0:14:03
2025-08-28 16:15:30.674 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 558/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 2.9, cls_loss: 0.9, lr: 2.488e-05, size: 352, ETA: 0:14:00
2025-08-28 16:15:33.803 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 558/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 2.470e-05, size: 416, ETA: 0:13:57
2025-08-28 16:15:36.872 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 558/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 2.452e-05, size: 480, ETA: 0:13:54
2025-08-28 16:15:39.966 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 558/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.8, lr: 2.434e-05, size: 512, ETA: 0:13:51
2025-08-28 16:15:43.012 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 558/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 2.7, iou_loss: 1.2, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.4, lr: 2.416e-05, size: 480, ETA: 0:13:48
2025-08-28 16:15:44.392 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:15:50.499 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:15:51.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:15:51.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5913
2025-08-28 16:15:51.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5348
2025-08-28 16:15:51.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4139
2025-08-28 16:15:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5133
2025-08-28 16:15:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:15:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:15:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.591
2025-08-28 16:15:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-08-28 16:15:51.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-08-28 16:15:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-08-28 16:15:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:15:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:15:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:15:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:15:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:15:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:15:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:15:51.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:15:51.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:15:52.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:15:52.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:15:53.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:15:53.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:15:54.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:15:54.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:15:55.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:15:55.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:15:56.051 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:15:56.051 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:15:56.051 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:15:56.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:15:56.059 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.90 ms, Average inference time: 7.21 ms

2025-08-28 16:15:56.060 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:15:56.233 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:15:56.344 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch559
2025-08-28 16:15:59.275 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 559/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 2.391e-05, size: 320, ETA: 0:13:44
2025-08-28 16:16:02.309 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 559/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 7.9, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.8, lr: 2.373e-05, size: 512, ETA: 0:13:41
2025-08-28 16:16:05.399 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 559/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 2.355e-05, size: 384, ETA: 0:13:38
2025-08-28 16:16:08.510 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 559/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.8, lr: 2.338e-05, size: 512, ETA: 0:13:34
2025-08-28 16:16:11.534 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 559/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 2.321e-05, size: 448, ETA: 0:13:31
2025-08-28 16:16:14.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 559/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 3.7, cls_loss: 0.7, lr: 2.303e-05, size: 288, ETA: 0:13:28
2025-08-28 16:16:15.984 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:16:22.153 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:16:23.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:16:23.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6041
2025-08-28 16:16:24.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5370
2025-08-28 16:16:24.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4174
2025-08-28 16:16:24.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5195
2025-08-28 16:16:24.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:16:24.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:16:24.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-08-28 16:16:24.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-08-28 16:16:24.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.417
2025-08-28 16:16:24.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.520
2025-08-28 16:16:24.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:16:24.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:16:24.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:16:24.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:16:24.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:16:24.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:16:24.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:16:24.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:16:24.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:16:25.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:16:25.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:16:26.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:16:27.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:16:28.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:16:29.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:16:30.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:16:31.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:16:32.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:16:32.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:16:32.128 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:16:32.128 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:16:32.135 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.40 ms, Average NMS time: 0.90 ms, Average inference time: 7.30 ms

2025-08-28 16:16:32.136 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:16:32.226 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:16:32.318 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch560
2025-08-28 16:16:35.102 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 560/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.137s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.1, cls_loss: 0.6, lr: 2.278e-05, size: 416, ETA: 0:13:24
2025-08-28 16:16:38.115 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 560/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.7, lr: 2.261e-05, size: 256, ETA: 0:13:21
2025-08-28 16:16:41.122 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 560/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.005s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.9, lr: 2.244e-05, size: 320, ETA: 0:13:18
2025-08-28 16:16:44.100 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 560/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.7, lr: 2.227e-05, size: 416, ETA: 0:13:15
2025-08-28 16:16:47.055 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 560/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 2.210e-05, size: 352, ETA: 0:13:12
2025-08-28 16:16:49.975 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 560/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 2.193e-05, size: 416, ETA: 0:13:09
2025-08-28 16:16:51.303 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:16:57.393 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:16:57.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:16:58.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6001
2025-08-28 16:16:58.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5025
2025-08-28 16:16:58.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4116
2025-08-28 16:16:58.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5048
2025-08-28 16:16:58.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:16:58.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:16:58.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-08-28 16:16:58.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-08-28 16:16:58.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-08-28 16:16:58.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-08-28 16:16:58.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:16:58.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:16:58.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:16:58.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:16:58.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:16:58.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:16:58.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:16:58.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:16:58.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:16:58.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:16:59.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:16:59.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:17:00.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:17:00.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:17:01.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:17:01.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:17:02.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:17:02.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:17:02.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:17:02.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 16:17:02.669 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:17:02.676 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.89 ms, Average inference time: 7.09 ms

2025-08-28 16:17:02.677 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:17:02.805 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:17:02.876 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch561
2025-08-28 16:17:05.787 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 561/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.3, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.8, lr: 2.168e-05, size: 256, ETA: 0:13:04
2025-08-28 16:17:08.784 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 561/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.9, lr: 2.152e-05, size: 384, ETA: 0:13:01
2025-08-28 16:17:11.772 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 561/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 2.135e-05, size: 352, ETA: 0:12:58
2025-08-28 16:17:14.740 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 561/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 0.8, cls_loss: 0.6, lr: 2.118e-05, size: 384, ETA: 0:12:55
2025-08-28 16:17:17.980 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 561/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.8, lr: 2.102e-05, size: 480, ETA: 0:12:52
2025-08-28 16:17:21.056 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 561/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 2.085e-05, size: 544, ETA: 0:12:49
2025-08-28 16:17:22.416 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:17:28.568 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:17:29.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:17:30.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5961
2025-08-28 16:17:30.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5362
2025-08-28 16:17:30.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3803
2025-08-28 16:17:30.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5042
2025-08-28 16:17:30.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:17:30.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:17:30.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.596
2025-08-28 16:17:30.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-08-28 16:17:30.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-08-28 16:17:30.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-08-28 16:17:30.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:17:30.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:17:30.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:17:30.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:17:30.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:17:30.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:17:30.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:17:30.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:17:30.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:17:30.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:17:31.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:17:32.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:17:33.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:17:34.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:17:34.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:17:35.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:17:36.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:17:37.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:17:37.085 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:17:37.085 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 16:17:37.085 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:17:37.092 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.92 ms, Average inference time: 7.08 ms

2025-08-28 16:17:37.093 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:17:37.214 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:17:37.290 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch562
2025-08-28 16:17:40.163 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 562/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.8, lr: 2.061e-05, size: 512, ETA: 0:12:44
2025-08-28 16:17:43.268 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 562/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 3.2, cls_loss: 0.6, lr: 2.045e-05, size: 576, ETA: 0:12:41
2025-08-28 16:17:46.409 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 562/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.7, lr: 2.029e-05, size: 256, ETA: 0:12:38
2025-08-28 16:17:49.376 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 562/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.7, lr: 2.012e-05, size: 512, ETA: 0:12:35
2025-08-28 16:17:52.341 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 562/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.3, l1_loss: 0.4, conf_loss: 1.5, cls_loss: 0.4, lr: 1.996e-05, size: 320, ETA: 0:12:32
2025-08-28 16:17:55.405 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 562/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 3.0, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 1.980e-05, size: 384, ETA: 0:12:29
2025-08-28 16:17:56.850 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:18:03.066 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:18:03.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:18:04.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6038
2025-08-28 16:18:04.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5249
2025-08-28 16:18:04.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4108
2025-08-28 16:18:04.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5132
2025-08-28 16:18:04.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:18:04.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:18:04.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-08-28 16:18:04.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 16:18:04.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-08-28 16:18:04.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-08-28 16:18:04.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:18:04.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:18:04.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:18:04.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:18:04.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:18:04.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:18:04.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:18:04.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:18:04.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:18:05.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:18:06.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:18:07.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:18:07.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:18:08.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:18:09.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:18:10.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:18:10.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:18:11.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:18:11.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:18:11.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:18:11.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:18:11.611 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.92 ms, Average inference time: 7.08 ms

2025-08-28 16:18:11.612 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:18:11.768 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:18:11.840 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch563
2025-08-28 16:18:14.764 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 563/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.5, lr: 1.957e-05, size: 544, ETA: 0:12:25
2025-08-28 16:18:17.858 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 563/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.941e-05, size: 576, ETA: 0:12:22
2025-08-28 16:18:21.150 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 563/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 9.3, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 4.0, cls_loss: 1.0, lr: 1.925e-05, size: 576, ETA: 0:12:19
2025-08-28 16:18:24.159 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 563/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.909e-05, size: 288, ETA: 0:12:16
2025-08-28 16:18:27.231 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 563/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.9, lr: 1.893e-05, size: 448, ETA: 0:12:12
2025-08-28 16:18:30.187 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 563/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.878e-05, size: 448, ETA: 0:12:09
2025-08-28 16:18:31.535 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:18:37.705 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:18:38.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:18:39.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6008
2025-08-28 16:18:39.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4991
2025-08-28 16:18:39.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4038
2025-08-28 16:18:39.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5012
2025-08-28 16:18:39.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:18:39.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:18:39.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.601
2025-08-28 16:18:39.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-08-28 16:18:39.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.404
2025-08-28 16:18:39.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-08-28 16:18:39.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:18:39.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:18:39.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:18:39.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:18:39.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:18:39.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:18:39.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:18:39.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:18:39.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:18:40.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:18:41.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:18:42.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:18:43.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:18:43.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:18:44.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:18:45.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:18:46.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:18:47.308 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:18:47.309 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 16:18:47.309 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 16:18:47.309 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:18:47.316 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.90 ms, Average inference time: 7.22 ms

2025-08-28 16:18:47.317 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:18:47.401 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:18:47.483 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch564
2025-08-28 16:18:50.561 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 564/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.6, lr: 1.855e-05, size: 480, ETA: 0:12:05
2025-08-28 16:18:53.498 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 564/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.5, lr: 1.840e-05, size: 288, ETA: 0:12:02
2025-08-28 16:18:56.676 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 564/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 1.824e-05, size: 448, ETA: 0:11:59
2025-08-28 16:18:59.809 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 564/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 4.2, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 1.809e-05, size: 288, ETA: 0:11:56
2025-08-28 16:19:02.947 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 564/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 1.3, cls_loss: 0.5, lr: 1.793e-05, size: 416, ETA: 0:11:53
2025-08-28 16:19:06.077 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 564/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.778e-05, size: 416, ETA: 0:11:50
2025-08-28 16:19:07.453 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:19:13.693 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:19:14.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:19:15.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6068
2025-08-28 16:19:15.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5295
2025-08-28 16:19:15.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4049
2025-08-28 16:19:15.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5137
2025-08-28 16:19:15.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:19:15.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:19:15.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-08-28 16:19:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-08-28 16:19:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-08-28 16:19:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-08-28 16:19:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:19:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:19:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:19:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:19:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:19:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:19:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:19:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:19:15.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:19:16.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:19:16.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:19:17.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:19:18.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:19:19.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:19:20.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:19:21.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:19:21.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:19:22.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:19:22.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:19:22.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:19:22.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:19:22.573 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.93 ms, Average inference time: 7.24 ms

2025-08-28 16:19:22.574 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:19:22.651 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:19:22.730 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch565
2025-08-28 16:19:25.528 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 565/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.138s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.756e-05, size: 416, ETA: 0:11:45
2025-08-28 16:19:28.564 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 565/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.5, lr: 1.741e-05, size: 416, ETA: 0:11:42
2025-08-28 16:19:31.553 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 565/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 1.726e-05, size: 384, ETA: 0:11:39
2025-08-28 16:19:34.504 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 565/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 1.711e-05, size: 416, ETA: 0:11:36
2025-08-28 16:19:37.484 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 565/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.696e-05, size: 288, ETA: 0:11:33
2025-08-28 16:19:40.406 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 565/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 1.6, lr: 1.681e-05, size: 288, ETA: 0:11:30
2025-08-28 16:19:41.803 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:19:47.942 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:19:49.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:19:49.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6074
2025-08-28 16:19:50.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5114
2025-08-28 16:19:50.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4170
2025-08-28 16:19:50.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5119
2025-08-28 16:19:50.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:19:50.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:19:50.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-08-28 16:19:50.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-08-28 16:19:50.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.417
2025-08-28 16:19:50.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.512
2025-08-28 16:19:50.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:19:50.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:19:50.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:19:50.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:19:50.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:19:50.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:19:50.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:19:50.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:19:50.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:19:51.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:19:52.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:19:52.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:19:53.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:19:54.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:19:55.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:19:56.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:19:57.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:19:58.752 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:19:58.752 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:19:58.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:19:58.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:19:58.760 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.93 ms, Average inference time: 7.18 ms

2025-08-28 16:19:58.761 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:19:58.839 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:19:58.920 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch566
2025-08-28 16:20:01.850 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 566/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.8, lr: 1.660e-05, size: 288, ETA: 0:11:26
2025-08-28 16:20:04.800 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 566/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 1.645e-05, size: 448, ETA: 0:11:22
2025-08-28 16:20:07.858 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 566/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.7, lr: 1.630e-05, size: 544, ETA: 0:11:19
2025-08-28 16:20:10.911 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 566/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 1.616e-05, size: 448, ETA: 0:11:16
2025-08-28 16:20:13.841 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 566/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.601e-05, size: 384, ETA: 0:11:13
2025-08-28 16:20:16.829 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 566/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 2.5, cls_loss: 0.7, lr: 1.587e-05, size: 256, ETA: 0:11:10
2025-08-28 16:20:18.158 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:20:24.333 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:20:25.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:20:25.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5996
2025-08-28 16:20:25.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5140
2025-08-28 16:20:25.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4121
2025-08-28 16:20:25.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5086
2025-08-28 16:20:25.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:20:25.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:20:25.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-08-28 16:20:25.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.514
2025-08-28 16:20:25.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-08-28 16:20:25.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.509
2025-08-28 16:20:25.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:20:25.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:20:25.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:20:25.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:20:25.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:20:25.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:20:25.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:20:25.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:20:25.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:20:26.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:20:27.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:20:27.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:20:28.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:20:29.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:20:29.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:20:30.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:20:30.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:20:31.608 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:20:31.608 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:20:31.608 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:20:31.608 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:20:31.615 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.91 ms, Average inference time: 7.11 ms

2025-08-28 16:20:31.616 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:20:31.691 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:20:31.770 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch567
2025-08-28 16:20:34.795 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 567/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.8, lr: 1.566e-05, size: 256, ETA: 0:11:06
2025-08-28 16:20:37.763 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 567/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 0.8, lr: 1.552e-05, size: 384, ETA: 0:11:03
2025-08-28 16:20:40.816 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 567/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.7, lr: 1.538e-05, size: 512, ETA: 0:11:00
2025-08-28 16:20:43.799 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 567/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.523e-05, size: 256, ETA: 0:10:57
2025-08-28 16:20:46.927 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 567/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 1.509e-05, size: 320, ETA: 0:10:54
2025-08-28 16:20:49.951 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 567/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.6, lr: 1.495e-05, size: 320, ETA: 0:10:51
2025-08-28 16:20:51.292 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:20:57.504 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:20:58.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:20:58.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5790
2025-08-28 16:20:58.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4840
2025-08-28 16:20:58.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4033
2025-08-28 16:20:58.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4888
2025-08-28 16:20:58.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:20:58.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:20:58.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-08-28 16:20:58.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-08-28 16:20:58.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-08-28 16:20:58.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.489
2025-08-28 16:20:58.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:20:58.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:20:58.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:20:58.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:20:58.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:20:58.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:20:58.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:20:58.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:20:58.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:20:59.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:20:59.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:21:00.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:21:00.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:21:00.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:21:01.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:21:02.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:21:02.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:21:03.064 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:21:03.065 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-08-28 16:21:03.065 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 16:21:03.065 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:21:03.071 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.90 ms, Average inference time: 7.21 ms

2025-08-28 16:21:03.072 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:21:03.158 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:21:03.237 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch568
2025-08-28 16:21:06.297 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 568/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 1.475e-05, size: 384, ETA: 0:10:46
2025-08-28 16:21:09.332 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 568/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 1.461e-05, size: 384, ETA: 0:10:43
2025-08-28 16:21:12.458 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 568/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.8, lr: 1.447e-05, size: 288, ETA: 0:10:40
2025-08-28 16:21:15.354 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 568/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 4.1, iou_loss: 2.1, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.6, lr: 1.434e-05, size: 352, ETA: 0:10:37
2025-08-28 16:21:18.393 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 568/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.0, iou_loss: 1.9, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 1.1, lr: 1.420e-05, size: 512, ETA: 0:10:34
2025-08-28 16:21:21.521 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 568/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.8, lr: 1.406e-05, size: 384, ETA: 0:10:31
2025-08-28 16:21:22.885 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:21:29.154 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:21:30.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:21:30.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6077
2025-08-28 16:21:30.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5330
2025-08-28 16:21:30.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4196
2025-08-28 16:21:30.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5201
2025-08-28 16:21:30.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:21:30.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:21:30.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-08-28 16:21:30.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-08-28 16:21:30.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.420
2025-08-28 16:21:30.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.520
2025-08-28 16:21:30.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:21:30.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:21:30.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:21:30.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:21:30.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:21:30.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:21:30.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:21:30.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:21:30.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:21:31.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:21:32.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:21:33.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:21:34.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:21:35.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:21:35.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:21:36.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:21:37.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:21:38.334 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:21:38.334 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:21:38.334 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:21:38.334 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:21:38.342 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.93 ms, Average inference time: 7.23 ms

2025-08-28 16:21:38.343 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:21:38.425 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:21:38.504 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch569
2025-08-28 16:21:41.454 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 569/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 0.8, cls_loss: 0.6, lr: 1.387e-05, size: 384, ETA: 0:10:26
2025-08-28 16:21:44.488 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 569/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.373e-05, size: 416, ETA: 0:10:23
2025-08-28 16:21:47.476 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 569/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 3.8, iou_loss: 2.1, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.6, lr: 1.360e-05, size: 288, ETA: 0:10:20
2025-08-28 16:21:50.605 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 569/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 1.347e-05, size: 576, ETA: 0:10:17
2025-08-28 16:21:53.738 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 569/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.5, lr: 1.334e-05, size: 288, ETA: 0:10:14
2025-08-28 16:21:56.756 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 569/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.320e-05, size: 256, ETA: 0:10:11
2025-08-28 16:21:58.063 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:22:04.183 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:22:05.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:22:05.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6000
2025-08-28 16:22:06.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5246
2025-08-28 16:22:06.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3910
2025-08-28 16:22:06.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5052
2025-08-28 16:22:06.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:22:06.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:22:06.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-08-28 16:22:06.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 16:22:06.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-08-28 16:22:06.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-08-28 16:22:06.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:22:06.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:22:06.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:22:06.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:22:06.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:22:06.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:22:06.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:22:06.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:22:06.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:22:07.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:22:07.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:22:08.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:22:09.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:22:10.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:22:11.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:22:12.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:22:13.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:22:13.932 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:22:13.932 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:22:13.932 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:22:13.932 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:22:13.939 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.92 ms, Average inference time: 7.21 ms

2025-08-28 16:22:13.940 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:22:14.064 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:22:14.139 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch570
2025-08-28 16:22:16.987 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 570/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.301e-05, size: 544, ETA: 0:10:07
2025-08-28 16:22:20.319 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 570/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.6, lr: 1.288e-05, size: 480, ETA: 0:10:04
2025-08-28 16:22:23.428 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 570/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.275e-05, size: 512, ETA: 0:10:01
2025-08-28 16:22:26.510 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 570/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.262e-05, size: 576, ETA: 0:09:58
2025-08-28 16:22:29.640 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 570/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.6, lr: 1.250e-05, size: 544, ETA: 0:09:54
2025-08-28 16:22:32.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 570/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.4, l1_loss: 1.3, conf_loss: 3.2, cls_loss: 0.8, lr: 1.237e-05, size: 256, ETA: 0:09:51
2025-08-28 16:22:33.980 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:22:40.278 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:22:41.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:22:41.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6089
2025-08-28 16:22:41.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5255
2025-08-28 16:22:41.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3831
2025-08-28 16:22:41.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5058
2025-08-28 16:22:41.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:22:41.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:22:41.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-08-28 16:22:41.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 16:22:41.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-08-28 16:22:41.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.506
2025-08-28 16:22:41.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:22:41.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:22:41.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:22:41.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:22:41.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:22:41.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:22:41.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:22:41.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:22:41.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:22:42.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:22:43.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:22:44.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:22:45.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:22:45.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:22:46.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:22:47.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:22:48.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:22:48.922 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:22:48.922 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:22:48.922 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:22:48.922 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:22:48.930 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.95 ms, Average inference time: 7.12 ms

2025-08-28 16:22:48.941 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:22:49.020 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:22:49.100 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch571
2025-08-28 16:22:51.983 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 571/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 1.218e-05, size: 256, ETA: 0:09:47
2025-08-28 16:22:55.019 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 571/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 1.206e-05, size: 288, ETA: 0:09:44
2025-08-28 16:22:58.104 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 571/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.005s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 1.193e-05, size: 448, ETA: 0:09:41
2025-08-28 16:23:01.091 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 571/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 1.181e-05, size: 352, ETA: 0:09:38
2025-08-28 16:23:04.052 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 571/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.8, lr: 1.168e-05, size: 352, ETA: 0:09:35
2025-08-28 16:23:07.036 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 571/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.7, lr: 1.156e-05, size: 384, ETA: 0:09:32
2025-08-28 16:23:08.446 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:23:14.549 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:23:15.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:23:15.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6073
2025-08-28 16:23:15.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5267
2025-08-28 16:23:15.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3962
2025-08-28 16:23:15.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5101
2025-08-28 16:23:15.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:23:15.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:23:15.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-08-28 16:23:15.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-08-28 16:23:15.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-08-28 16:23:15.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.510
2025-08-28 16:23:15.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:23:15.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:23:15.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:23:15.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:23:15.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:23:15.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:23:15.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:23:15.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:23:15.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:23:16.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:23:17.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:23:17.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:23:18.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:23:19.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:23:19.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:23:20.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:23:21.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:23:21.648 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:23:21.648 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:23:21.648 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:23:21.648 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:23:21.655 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.92 ms, Average inference time: 7.19 ms

2025-08-28 16:23:21.656 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:23:21.737 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:23:21.818 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch572
2025-08-28 16:23:24.755 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 572/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.7, lr: 1.138e-05, size: 416, ETA: 0:09:27
2025-08-28 16:23:27.675 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 572/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.126e-05, size: 352, ETA: 0:09:24
2025-08-28 16:23:30.678 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 572/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.005s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 1.114e-05, size: 256, ETA: 0:09:21
2025-08-28 16:23:33.848 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 572/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 8.6, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 3.9, cls_loss: 0.7, lr: 1.102e-05, size: 448, ETA: 0:09:18
2025-08-28 16:23:36.817 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 572/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 1.090e-05, size: 256, ETA: 0:09:15
2025-08-28 16:23:40.103 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 572/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.078e-05, size: 576, ETA: 0:09:12
2025-08-28 16:23:41.504 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:23:47.545 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:23:48.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:23:48.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5986
2025-08-28 16:23:48.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5279
2025-08-28 16:23:48.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4096
2025-08-28 16:23:48.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5120
2025-08-28 16:23:48.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:23:48.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:23:48.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-08-28 16:23:48.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-08-28 16:23:48.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-08-28 16:23:48.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.512
2025-08-28 16:23:48.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:23:48.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:23:48.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:23:48.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:23:48.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:23:48.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:23:48.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:23:48.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:23:48.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:23:49.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:23:50.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:23:50.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:23:51.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:23:51.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:23:52.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:23:52.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:23:53.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:23:53.890 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:23:53.890 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:23:53.890 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:23:53.890 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:23:53.897 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.88 ms, Average inference time: 7.08 ms

2025-08-28 16:23:53.898 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:23:54.028 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:23:54.101 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch573
2025-08-28 16:23:57.005 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 573/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.8, lr: 1.061e-05, size: 448, ETA: 0:09:08
2025-08-28 16:24:00.147 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 573/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.5, lr: 1.049e-05, size: 480, ETA: 0:09:05
2025-08-28 16:24:03.345 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 573/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.8, lr: 1.037e-05, size: 576, ETA: 0:09:01
2025-08-28 16:24:06.426 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 573/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 1.026e-05, size: 320, ETA: 0:08:58
2025-08-28 16:24:09.331 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 573/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 1.014e-05, size: 288, ETA: 0:08:55
2025-08-28 16:24:12.450 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 573/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.9, lr: 1.003e-05, size: 576, ETA: 0:08:52
2025-08-28 16:24:13.808 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:24:19.829 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:24:20.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:24:21.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6057
2025-08-28 16:24:21.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5203
2025-08-28 16:24:21.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4028
2025-08-28 16:24:21.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5096
2025-08-28 16:24:21.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:24:21.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:24:21.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.606
2025-08-28 16:24:21.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-08-28 16:24:21.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-08-28 16:24:21.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.510
2025-08-28 16:24:21.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:24:21.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:24:21.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:24:21.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:24:21.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:24:21.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:24:21.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:24:21.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:24:21.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:24:22.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:24:22.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:24:23.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:24:24.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:24:25.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:24:26.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:24:26.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:24:27.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:24:28.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:24:28.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 16:24:28.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:24:28.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:24:28.173 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.93 ms, Average inference time: 7.09 ms

2025-08-28 16:24:28.176 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:24:28.265 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:24:28.352 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch574
2025-08-28 16:24:31.290 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 574/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 9.862e-06, size: 352, ETA: 0:08:48
2025-08-28 16:24:34.240 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 574/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 9.749e-06, size: 448, ETA: 0:08:45
2025-08-28 16:24:37.303 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 574/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.006s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.8, lr: 9.636e-06, size: 256, ETA: 0:08:42
2025-08-28 16:24:40.336 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 574/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 1.5, conf_loss: 2.7, cls_loss: 0.7, lr: 9.524e-06, size: 480, ETA: 0:08:39
2025-08-28 16:24:43.377 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 574/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 9.413e-06, size: 352, ETA: 0:08:36
2025-08-28 16:24:46.384 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 574/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.6, lr: 9.302e-06, size: 544, ETA: 0:08:33
2025-08-28 16:24:47.731 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:24:53.810 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:24:54.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:24:54.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5979
2025-08-28 16:24:54.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5161
2025-08-28 16:24:55.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3681
2025-08-28 16:24:55.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4940
2025-08-28 16:24:55.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:24:55.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:24:55.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-08-28 16:24:55.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-08-28 16:24:55.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-08-28 16:24:55.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-08-28 16:24:55.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:24:55.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:24:55.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:24:55.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:24:55.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:24:55.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:24:55.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:24:55.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:24:55.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:24:55.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:24:56.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:24:56.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:24:57.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:24:57.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:24:58.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:24:58.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:24:59.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:24:59.731 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:24:59.731 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:24:59.731 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-08-28 16:24:59.732 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:24:59.738 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.89 ms, Average inference time: 7.13 ms

2025-08-28 16:24:59.738 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:24:59.824 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:24:59.915 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch575
2025-08-28 16:25:02.838 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 575/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 9.142e-06, size: 512, ETA: 0:08:28
2025-08-28 16:25:05.902 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 575/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 9.033e-06, size: 256, ETA: 0:08:25
2025-08-28 16:25:08.940 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 575/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 16.5, iou_loss: 4.1, l1_loss: 3.4, conf_loss: 8.1, cls_loss: 0.9, lr: 8.925e-06, size: 544, ETA: 0:08:22
2025-08-28 16:25:11.961 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 575/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 8.817e-06, size: 352, ETA: 0:08:19
2025-08-28 16:25:15.021 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 575/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 8.709e-06, size: 416, ETA: 0:08:16
2025-08-28 16:25:18.063 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 575/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 8.603e-06, size: 288, ETA: 0:08:13
2025-08-28 16:25:19.365 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:25:25.605 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:25:26.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:25:27.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5893
2025-08-28 16:25:27.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5072
2025-08-28 16:25:27.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4029
2025-08-28 16:25:27.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4998
2025-08-28 16:25:27.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:25:27.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:25:27.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-08-28 16:25:27.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-08-28 16:25:27.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-08-28 16:25:27.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-08-28 16:25:27.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:25:27.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:25:27.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:25:27.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:25:27.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:25:27.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:25:27.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:25:27.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:25:27.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:25:28.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:25:29.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:25:30.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:25:31.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:25:32.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:25:33.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:25:34.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:25:35.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:25:36.319 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:25:36.320 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:25:36.320 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 16:25:36.320 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:25:36.329 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.94 ms, Average inference time: 7.11 ms

2025-08-28 16:25:36.329 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:25:36.474 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:25:36.546 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch576
2025-08-28 16:25:39.483 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 576/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 8.450e-06, size: 352, ETA: 0:08:08
2025-08-28 16:25:42.521 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 576/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.8, lr: 8.345e-06, size: 320, ETA: 0:08:05
2025-08-28 16:25:45.448 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 576/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 8.240e-06, size: 256, ETA: 0:08:02
2025-08-28 16:25:48.453 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 576/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 8.137e-06, size: 448, ETA: 0:07:59
2025-08-28 16:25:51.484 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 576/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.8, lr: 8.034e-06, size: 544, ETA: 0:07:56
2025-08-28 16:25:54.481 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 576/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 7.931e-06, size: 352, ETA: 0:07:53
2025-08-28 16:25:55.805 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:26:02.011 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:26:03.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:26:03.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6139
2025-08-28 16:26:03.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5193
2025-08-28 16:26:03.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4137
2025-08-28 16:26:03.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5156
2025-08-28 16:26:03.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:26:03.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:26:03.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.614
2025-08-28 16:26:03.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.519
2025-08-28 16:26:03.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-08-28 16:26:03.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.516
2025-08-28 16:26:03.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:26:03.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:26:03.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:26:03.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:26:03.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:26:03.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:26:03.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:26:03.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:26:03.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:26:04.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:26:05.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:26:06.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:26:07.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:26:08.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:26:08.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:26:09.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:26:10.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:26:11.426 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:26:11.426 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:26:11.426 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:26:11.426 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:26:11.433 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.90 ms, Average inference time: 7.15 ms

2025-08-28 16:26:11.434 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:26:11.514 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:26:11.593 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch577
2025-08-28 16:26:14.462 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 577/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.6, lr: 7.784e-06, size: 480, ETA: 0:07:49
2025-08-28 16:26:17.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 577/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 1.7, cls_loss: 0.6, lr: 7.683e-06, size: 480, ETA: 0:07:46
2025-08-28 16:26:20.553 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 577/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 7.583e-06, size: 384, ETA: 0:07:43
2025-08-28 16:26:23.594 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 577/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 1.0, lr: 7.484e-06, size: 416, ETA: 0:07:40
2025-08-28 16:26:26.566 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 577/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 7.385e-06, size: 448, ETA: 0:07:37
2025-08-28 16:26:29.632 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 577/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 7.287e-06, size: 288, ETA: 0:07:33
2025-08-28 16:26:30.985 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:26:37.178 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:26:38.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:26:38.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6076
2025-08-28 16:26:38.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4930
2025-08-28 16:26:38.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4078
2025-08-28 16:26:38.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5028
2025-08-28 16:26:38.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:26:38.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:26:38.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-08-28 16:26:38.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-08-28 16:26:38.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.408
2025-08-28 16:26:38.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-08-28 16:26:38.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:26:38.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:26:38.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:26:38.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:26:38.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:26:38.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:26:38.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:26:38.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:26:38.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:26:39.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:26:40.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:26:40.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:26:41.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:26:41.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:26:42.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:26:43.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:26:43.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:26:44.553 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:26:44.553 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:26:44.554 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 16:26:44.554 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:26:44.561 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.93 ms, Average inference time: 7.14 ms

2025-08-28 16:26:44.562 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:26:44.644 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:26:44.765 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch578
2025-08-28 16:26:47.733 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 578/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.7, lr: 7.145e-06, size: 480, ETA: 0:07:29
2025-08-28 16:26:50.766 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 578/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.5, lr: 7.049e-06, size: 512, ETA: 0:07:26
2025-08-28 16:26:53.789 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 578/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 6.953e-06, size: 480, ETA: 0:07:23
2025-08-28 16:26:56.841 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 578/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 1.4, cls_loss: 0.5, lr: 6.858e-06, size: 448, ETA: 0:07:20
2025-08-28 16:26:59.806 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 578/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 6.763e-06, size: 352, ETA: 0:07:17
2025-08-28 16:27:02.941 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 578/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 6.669e-06, size: 384, ETA: 0:07:14
2025-08-28 16:27:04.278 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:27:10.437 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:27:11.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:27:11.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6081
2025-08-28 16:27:11.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5434
2025-08-28 16:27:11.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4143
2025-08-28 16:27:11.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5219
2025-08-28 16:27:11.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:27:11.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:27:11.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-08-28 16:27:11.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-08-28 16:27:11.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-08-28 16:27:11.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.522
2025-08-28 16:27:11.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:27:11.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:27:11.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:27:11.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:27:11.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:27:11.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:27:11.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:27:11.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:27:11.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:27:12.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:27:13.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:27:13.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:27:14.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:27:15.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:27:15.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:27:16.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:27:16.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:27:17.506 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:27:17.507 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:27:17.507 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:27:17.507 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:27:17.514 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.92 ms, Average inference time: 7.23 ms

2025-08-28 16:27:17.515 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:27:17.635 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:27:17.739 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch579
2025-08-28 16:27:20.618 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 579/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 6.534e-06, size: 416, ETA: 0:07:09
2025-08-28 16:27:23.875 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 579/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.8, lr: 6.442e-06, size: 480, ETA: 0:07:06
2025-08-28 16:27:26.969 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 579/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 6.350e-06, size: 416, ETA: 0:07:03
2025-08-28 16:27:30.149 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 579/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 6.259e-06, size: 320, ETA: 0:07:00
2025-08-28 16:27:33.051 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 579/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 6.169e-06, size: 352, ETA: 0:06:57
2025-08-28 16:27:35.957 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 579/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 6.079e-06, size: 384, ETA: 0:06:54
2025-08-28 16:27:37.296 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:27:43.473 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:27:44.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:27:45.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6144
2025-08-28 16:27:45.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5249
2025-08-28 16:27:45.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4067
2025-08-28 16:27:45.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5153
2025-08-28 16:27:45.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:27:45.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:27:45.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.614
2025-08-28 16:27:45.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-08-28 16:27:45.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-08-28 16:27:45.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.515
2025-08-28 16:27:45.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:27:45.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:27:45.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:27:45.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:27:45.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:27:45.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:27:45.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:27:45.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:27:45.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:27:46.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:27:47.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:27:47.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:27:48.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:27:49.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:27:50.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:27:51.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:27:52.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:27:52.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:27:52.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:27:52.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:27:52.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:27:52.965 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.91 ms, Average inference time: 7.07 ms

2025-08-28 16:27:52.966 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:27:53.084 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:27:53.191 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch580
2025-08-28 16:27:56.137 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 580/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 5.950e-06, size: 384, ETA: 0:06:50
2025-08-28 16:27:59.222 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 580/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 9.1, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 4.2, cls_loss: 3.2, lr: 5.862e-06, size: 256, ETA: 0:06:47
2025-08-28 16:28:02.359 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 580/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.7, lr: 5.775e-06, size: 480, ETA: 0:06:44
2025-08-28 16:28:05.409 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 580/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.5, lr: 5.688e-06, size: 544, ETA: 0:06:41
2025-08-28 16:28:08.582 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 580/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 5.602e-06, size: 576, ETA: 0:06:37
2025-08-28 16:28:11.604 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 580/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 5.516e-06, size: 576, ETA: 0:06:34
2025-08-28 16:28:13.011 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:28:19.247 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:28:19.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:28:20.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6081
2025-08-28 16:28:20.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5432
2025-08-28 16:28:20.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4126
2025-08-28 16:28:20.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5213
2025-08-28 16:28:20.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:28:20.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:28:20.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-08-28 16:28:20.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-08-28 16:28:20.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-08-28 16:28:20.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.521
2025-08-28 16:28:20.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:28:20.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:28:20.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:28:20.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:28:20.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:28:20.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:28:20.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:28:20.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:28:20.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:28:21.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:28:21.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:28:22.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:28:22.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:28:23.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:28:23.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:28:24.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:28:24.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:28:25.320 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:28:25.320 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:28:25.320 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:28:25.320 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:28:25.327 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.38 ms, Average NMS time: 0.91 ms, Average inference time: 7.29 ms

2025-08-28 16:28:25.328 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:28:25.410 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:28:25.489 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch581
2025-08-28 16:28:28.331 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 581/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 5.394e-06, size: 352, ETA: 0:06:30
2025-08-28 16:28:31.342 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 581/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 4.9, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 5.310e-06, size: 512, ETA: 0:06:27
2025-08-28 16:28:34.333 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 581/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.4, cls_loss: 0.6, lr: 5.226e-06, size: 352, ETA: 0:06:24
2025-08-28 16:28:37.391 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 581/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.2, cls_loss: 0.6, lr: 5.144e-06, size: 576, ETA: 0:06:21
2025-08-28 16:28:40.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 581/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 1.7, lr: 5.062e-06, size: 352, ETA: 0:06:18
2025-08-28 16:28:43.371 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 581/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 4.981e-06, size: 384, ETA: 0:06:15
2025-08-28 16:28:44.669 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:28:50.763 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:28:51.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:28:52.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6192
2025-08-28 16:28:52.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5257
2025-08-28 16:28:52.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4012
2025-08-28 16:28:52.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5154
2025-08-28 16:28:52.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:28:52.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:28:52.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.619
2025-08-28 16:28:52.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-08-28 16:28:52.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-08-28 16:28:52.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.515
2025-08-28 16:28:52.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:28:52.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:28:52.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:28:52.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:28:52.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:28:52.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:28:52.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:28:52.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:28:52.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:28:52.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:28:53.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:28:54.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:28:54.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:28:55.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:28:56.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:28:56.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:28:57.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:28:57.983 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:28:57.983 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:28:57.983 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:28:57.984 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:28:57.991 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.06 ms, Average NMS time: 0.88 ms, Average inference time: 6.94 ms

2025-08-28 16:28:57.992 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:28:58.118 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:28:58.230 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch582
2025-08-28 16:29:01.211 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 582/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.2, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.6, lr: 4.864e-06, size: 384, ETA: 0:06:10
2025-08-28 16:29:04.265 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 582/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 4.784e-06, size: 352, ETA: 0:06:07
2025-08-28 16:29:07.350 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 582/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.5, lr: 4.706e-06, size: 416, ETA: 0:06:04
2025-08-28 16:29:10.402 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 582/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 1.0, lr: 4.627e-06, size: 352, ETA: 0:06:01
2025-08-28 16:29:13.397 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 582/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.6, lr: 4.549e-06, size: 512, ETA: 0:05:58
2025-08-28 16:29:16.323 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 582/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.7, lr: 4.472e-06, size: 416, ETA: 0:05:55
2025-08-28 16:29:17.708 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:29:23.959 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:29:24.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:29:24.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5933
2025-08-28 16:29:24.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5278
2025-08-28 16:29:24.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3937
2025-08-28 16:29:24.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5049
2025-08-28 16:29:24.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:29:24.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:29:24.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-08-28 16:29:24.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-08-28 16:29:24.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-08-28 16:29:24.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-08-28 16:29:24.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:29:24.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:29:24.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:29:24.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:29:24.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:29:24.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:29:24.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:29:24.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:29:24.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:29:25.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:29:25.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:29:26.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:29:26.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:29:27.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:29:27.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:29:28.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:29:28.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:29:28.970 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:29:28.971 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:29:28.971 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 16:29:28.971 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:29:28.977 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.89 ms, Average inference time: 7.14 ms

2025-08-28 16:29:28.978 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:29:29.066 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:29:29.142 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch583
2025-08-28 16:29:32.052 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 583/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.8, lr: 4.362e-06, size: 352, ETA: 0:05:51
2025-08-28 16:29:35.009 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 583/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.9, lr: 4.287e-06, size: 256, ETA: 0:05:48
2025-08-28 16:29:38.057 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 583/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 4.212e-06, size: 576, ETA: 0:05:44
2025-08-28 16:29:41.152 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 583/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.7, lr: 4.138e-06, size: 448, ETA: 0:05:41
2025-08-28 16:29:44.131 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 583/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.9, lr: 4.064e-06, size: 480, ETA: 0:05:38
2025-08-28 16:29:47.201 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 583/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 0.8, cls_loss: 0.6, lr: 3.991e-06, size: 320, ETA: 0:05:35
2025-08-28 16:29:48.546 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:29:54.878 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:29:55.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:29:56.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5981
2025-08-28 16:29:56.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5071
2025-08-28 16:29:56.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4016
2025-08-28 16:29:56.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5022
2025-08-28 16:29:56.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:29:56.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:29:56.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-08-28 16:29:56.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-08-28 16:29:56.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-08-28 16:29:56.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-08-28 16:29:56.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:29:56.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:29:56.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:29:56.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:29:56.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:29:56.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:29:56.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:29:56.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:29:56.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:29:56.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:29:57.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:29:58.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:29:58.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:29:59.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:29:59.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:30:00.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:30:01.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:30:01.705 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:30:01.705 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:30:01.705 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 16:30:01.705 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:30:01.712 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.90 ms, Average inference time: 7.10 ms

2025-08-28 16:30:01.713 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:30:01.794 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:30:01.874 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch584
2025-08-28 16:30:04.790 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 584/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.9, lr: 3.887e-06, size: 256, ETA: 0:05:31
2025-08-28 16:30:07.856 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 584/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 3.816e-06, size: 576, ETA: 0:05:28
2025-08-28 16:30:10.913 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 584/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.9, lr: 3.745e-06, size: 320, ETA: 0:05:25
2025-08-28 16:30:13.875 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 584/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.6, lr: 3.676e-06, size: 448, ETA: 0:05:22
2025-08-28 16:30:16.903 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 584/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 1.1, lr: 3.606e-06, size: 512, ETA: 0:05:19
2025-08-28 16:30:19.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 584/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.9, lr: 3.538e-06, size: 480, ETA: 0:05:16
2025-08-28 16:30:21.288 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:30:27.460 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:30:28.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:30:28.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6010
2025-08-28 16:30:28.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5356
2025-08-28 16:30:28.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4361
2025-08-28 16:30:28.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5242
2025-08-28 16:30:28.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:30:28.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:30:28.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.601
2025-08-28 16:30:28.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-08-28 16:30:28.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.436
2025-08-28 16:30:28.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.524
2025-08-28 16:30:28.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:30:28.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:30:28.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:30:28.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:30:28.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:30:28.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:30:28.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:30:28.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:30:28.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:30:29.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:30:30.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:30:30.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:30:31.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:30:32.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:30:32.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:30:33.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:30:34.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:30:34.817 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:30:34.818 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:30:34.818 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:30:34.818 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:30:34.825 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.93 ms, Average inference time: 7.12 ms

2025-08-28 16:30:34.826 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:30:34.902 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:30:35.040 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch585
2025-08-28 16:30:37.886 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 585/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 10.3, iou_loss: 3.6, l1_loss: 1.4, conf_loss: 4.5, cls_loss: 0.8, lr: 3.440e-06, size: 256, ETA: 0:05:11
2025-08-28 16:30:40.903 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 585/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 3.373e-06, size: 448, ETA: 0:05:08
2025-08-28 16:30:43.850 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 585/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 3.306e-06, size: 288, ETA: 0:05:05
2025-08-28 16:30:47.048 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 585/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.7, lr: 3.241e-06, size: 448, ETA: 0:05:02
2025-08-28 16:30:50.137 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 585/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 3.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 0.6, cls_loss: 0.6, lr: 3.176e-06, size: 352, ETA: 0:04:59
2025-08-28 16:30:53.166 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 585/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.7, lr: 3.111e-06, size: 288, ETA: 0:04:56
2025-08-28 16:30:54.480 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:31:00.485 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:31:01.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:31:01.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6093
2025-08-28 16:31:01.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5159
2025-08-28 16:31:01.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4121
2025-08-28 16:31:01.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5124
2025-08-28 16:31:01.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:31:01.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:31:01.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-08-28 16:31:01.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-08-28 16:31:01.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-08-28 16:31:01.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.512
2025-08-28 16:31:01.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:31:01.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:31:01.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:31:01.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:31:01.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:31:01.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:31:01.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:31:01.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:31:01.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:31:02.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:31:03.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:31:03.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:31:04.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:31:04.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:31:05.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:31:06.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:31:06.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:31:07.211 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:31:07.211 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:31:07.211 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:31:07.211 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:31:07.218 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.91 ms, Average inference time: 7.08 ms

2025-08-28 16:31:07.219 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:31:07.304 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:31:07.400 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch586
2025-08-28 16:31:10.298 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 586/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.8, lr: 3.019e-06, size: 320, ETA: 0:04:52
2025-08-28 16:31:13.487 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 586/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 2.957e-06, size: 512, ETA: 0:04:48
2025-08-28 16:31:16.471 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 586/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.8, lr: 2.895e-06, size: 544, ETA: 0:04:45
2025-08-28 16:31:19.652 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 586/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.7, lr: 2.833e-06, size: 416, ETA: 0:04:42
2025-08-28 16:31:22.799 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 586/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.5, lr: 2.772e-06, size: 512, ETA: 0:04:39
2025-08-28 16:31:25.746 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 586/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 2.712e-06, size: 320, ETA: 0:04:36
2025-08-28 16:31:27.067 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:31:33.352 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:31:34.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:31:34.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6135
2025-08-28 16:31:34.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5288
2025-08-28 16:31:35.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4115
2025-08-28 16:31:35.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5180
2025-08-28 16:31:35.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:31:35.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:31:35.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.614
2025-08-28 16:31:35.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-08-28 16:31:35.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-08-28 16:31:35.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.518
2025-08-28 16:31:35.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:31:35.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:31:35.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:31:35.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:31:35.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:31:35.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:31:35.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:31:35.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:31:35.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:31:35.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:31:36.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:31:37.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:31:38.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:31:38.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:31:39.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:31:40.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:31:41.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:31:41.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:31:41.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:31:41.890 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:31:41.891 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:31:41.903 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.95 ms, Average inference time: 7.17 ms

2025-08-28 16:31:41.904 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:31:42.011 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:31:42.085 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch587
2025-08-28 16:31:44.935 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 587/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 2.626e-06, size: 288, ETA: 0:04:32
2025-08-28 16:31:47.887 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 587/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.005s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 2.568e-06, size: 448, ETA: 0:04:29
2025-08-28 16:31:50.881 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 587/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.6, lr: 2.510e-06, size: 544, ETA: 0:04:26
2025-08-28 16:31:53.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 587/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.6, lr: 2.453e-06, size: 256, ETA: 0:04:23
2025-08-28 16:31:57.007 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 587/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.7, lr: 2.396e-06, size: 384, ETA: 0:04:20
2025-08-28 16:32:00.038 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 587/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.7, lr: 2.341e-06, size: 480, ETA: 0:04:17
2025-08-28 16:32:01.404 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:32:07.642 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:32:08.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:32:09.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6131
2025-08-28 16:32:09.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5350
2025-08-28 16:32:09.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4055
2025-08-28 16:32:09.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5179
2025-08-28 16:32:09.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:32:09.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:32:09.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.613
2025-08-28 16:32:09.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-08-28 16:32:09.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-08-28 16:32:09.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.518
2025-08-28 16:32:09.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:32:09.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:32:09.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:32:09.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:32:09.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:32:09.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:32:09.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:32:09.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:32:09.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:32:09.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:32:10.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:32:11.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:32:12.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:32:12.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:32:13.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:32:14.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:32:14.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:32:15.510 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:32:15.510 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:32:15.510 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:32:15.510 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:32:15.517 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.90 ms, Average inference time: 7.14 ms

2025-08-28 16:32:15.518 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:32:15.600 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:32:15.680 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch588
2025-08-28 16:32:18.599 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 588/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.8, cls_loss: 0.8, lr: 2.261e-06, size: 576, ETA: 0:04:12
2025-08-28 16:32:21.713 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 588/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.9, cls_loss: 0.6, lr: 2.207e-06, size: 448, ETA: 0:04:09
2025-08-28 16:32:24.681 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 588/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.8, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.5, lr: 2.153e-06, size: 480, ETA: 0:04:06
2025-08-28 16:32:27.669 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 588/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 2.100e-06, size: 416, ETA: 0:04:03
2025-08-28 16:32:30.640 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 588/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 2.048e-06, size: 384, ETA: 0:04:00
2025-08-28 16:32:33.755 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 588/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 1.996e-06, size: 544, ETA: 0:03:57
2025-08-28 16:32:35.150 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:32:41.310 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:32:41.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:32:42.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6133
2025-08-28 16:32:42.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5285
2025-08-28 16:32:42.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4187
2025-08-28 16:32:42.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5202
2025-08-28 16:32:42.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:32:42.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:32:42.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.613
2025-08-28 16:32:42.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-08-28 16:32:42.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-08-28 16:32:42.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.520
2025-08-28 16:32:42.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:32:42.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:32:42.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:32:42.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:32:42.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:32:42.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:32:42.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:32:42.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:32:42.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:32:43.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:32:43.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:32:44.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:32:44.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:32:45.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:32:45.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:32:46.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:32:46.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:32:47.462 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:32:47.463 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:32:47.463 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:32:47.463 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:32:47.469 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.90 ms, Average inference time: 7.13 ms

2025-08-28 16:32:47.471 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:32:47.553 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:32:47.642 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch589
2025-08-28 16:32:50.607 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 589/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 1.923e-06, size: 352, ETA: 0:03:52
2025-08-28 16:32:53.740 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 589/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 8.2, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 3.6, cls_loss: 1.0, lr: 1.873e-06, size: 544, ETA: 0:03:49
2025-08-28 16:32:56.855 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 589/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.7, lr: 1.823e-06, size: 384, ETA: 0:03:46
2025-08-28 16:32:59.884 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 589/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 1.8, cls_loss: 0.8, lr: 1.775e-06, size: 576, ETA: 0:03:43
2025-08-28 16:33:02.917 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 589/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.5, lr: 1.727e-06, size: 320, ETA: 0:03:40
2025-08-28 16:33:05.945 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 589/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 3.3, cls_loss: 0.8, lr: 1.679e-06, size: 416, ETA: 0:03:37
2025-08-28 16:33:07.285 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:33:13.385 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:33:14.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:33:14.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6166
2025-08-28 16:33:14.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5023
2025-08-28 16:33:14.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4104
2025-08-28 16:33:14.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5098
2025-08-28 16:33:14.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:33:14.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:33:14.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.617
2025-08-28 16:33:14.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-08-28 16:33:14.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-08-28 16:33:14.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.510
2025-08-28 16:33:14.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:33:14.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:33:14.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:33:14.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:33:14.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:33:14.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:33:14.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:33:14.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:33:14.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:33:15.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:33:15.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:33:16.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:33:17.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:33:17.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:33:18.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:33:18.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:33:19.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:33:19.866 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:33:19.867 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:33:19.867 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:33:19.867 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:33:19.874 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.91 ms, Average inference time: 7.13 ms

2025-08-28 16:33:19.875 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:33:19.963 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:33:20.082 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch590
2025-08-28 16:33:22.965 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 590/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 1.612e-06, size: 448, ETA: 0:03:33
2025-08-28 16:33:25.994 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 590/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 3.0, cls_loss: 0.9, lr: 1.566e-06, size: 352, ETA: 0:03:30
2025-08-28 16:33:28.962 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 590/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.8, lr: 1.521e-06, size: 320, ETA: 0:03:27
2025-08-28 16:33:32.057 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 590/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.8, lr: 1.477e-06, size: 448, ETA: 0:03:24
2025-08-28 16:33:35.309 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 590/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.8, lr: 1.433e-06, size: 256, ETA: 0:03:21
2025-08-28 16:33:38.370 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 590/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.7, lr: 1.390e-06, size: 320, ETA: 0:03:18
2025-08-28 16:33:39.679 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:33:45.946 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:33:46.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:33:47.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6073
2025-08-28 16:33:47.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5242
2025-08-28 16:33:47.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3922
2025-08-28 16:33:47.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5079
2025-08-28 16:33:47.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:33:47.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:33:47.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-08-28 16:33:47.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-08-28 16:33:47.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-08-28 16:33:47.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.508
2025-08-28 16:33:47.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:33:47.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:33:47.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:33:47.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:33:47.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:33:47.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:33:47.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:33:47.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:33:47.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:33:48.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:33:49.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:33:49.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:33:50.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:33:51.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:33:52.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:33:53.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:33:53.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:33:54.544 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:33:54.544 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:33:54.545 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:33:54.545 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:33:54.557 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.92 ms, Average inference time: 7.13 ms

2025-08-28 16:33:54.558 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:33:54.674 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:33:54.789 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch591
2025-08-28 16:33:57.653 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 591/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.328e-06, size: 320, ETA: 0:03:13
2025-08-28 16:34:00.758 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 591/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 1.287e-06, size: 416, ETA: 0:03:10
2025-08-28 16:34:03.800 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 591/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 1.246e-06, size: 544, ETA: 0:03:07
2025-08-28 16:34:06.826 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 591/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 1.206e-06, size: 288, ETA: 0:03:04
2025-08-28 16:34:09.724 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 591/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 1.166e-06, size: 288, ETA: 0:03:01
2025-08-28 16:34:12.837 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 591/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 1.127e-06, size: 352, ETA: 0:02:58
2025-08-28 16:34:14.167 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:34:20.302 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:34:21.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:34:21.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6118
2025-08-28 16:34:21.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5182
2025-08-28 16:34:21.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4081
2025-08-28 16:34:21.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5127
2025-08-28 16:34:21.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:34:21.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:34:21.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.612
2025-08-28 16:34:21.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.518
2025-08-28 16:34:21.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.408
2025-08-28 16:34:21.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-08-28 16:34:21.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:34:21.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:34:21.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:34:21.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:34:21.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:34:21.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:34:21.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:34:21.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:34:21.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:34:22.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:34:23.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:34:23.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:34:24.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:34:25.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:34:26.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:34:26.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:34:27.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:34:28.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:34:28.037 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:34:28.037 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:34:28.037 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:34:28.045 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.91 ms, Average inference time: 7.06 ms

2025-08-28 16:34:28.046 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:34:28.133 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:34:28.214 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch592
2025-08-28 16:34:31.048 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 592/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 1.072e-06, size: 288, ETA: 0:02:53
2025-08-28 16:34:34.127 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 592/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 1.035e-06, size: 480, ETA: 0:02:50
2025-08-28 16:34:37.078 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 592/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.7, lr: 9.984e-07, size: 544, ETA: 0:02:47
2025-08-28 16:34:40.088 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 592/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 9.624e-07, size: 352, ETA: 0:02:44
2025-08-28 16:34:43.243 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 592/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 9.272e-07, size: 512, ETA: 0:02:41
2025-08-28 16:34:46.224 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 592/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 8.925e-07, size: 320, ETA: 0:02:38
2025-08-28 16:34:47.665 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:34:53.963 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:34:54.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:34:55.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6055
2025-08-28 16:34:55.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5148
2025-08-28 16:34:55.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4141
2025-08-28 16:34:55.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5114
2025-08-28 16:34:55.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:34:55.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:34:55.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.606
2025-08-28 16:34:55.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-08-28 16:34:55.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-08-28 16:34:55.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-08-28 16:34:55.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:34:55.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:34:55.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:34:55.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:34:55.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:34:55.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:34:55.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:34:55.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:34:55.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:34:56.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:34:56.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:34:57.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:34:58.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:34:58.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:34:59.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:35:00.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:35:00.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:35:01.474 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:35:01.474 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:35:01.474 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-08-28 16:35:01.474 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:35:01.481 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.95 ms, Average inference time: 7.07 ms

2025-08-28 16:35:01.482 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:35:01.637 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:35:01.709 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch593
2025-08-28 16:35:04.675 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 593/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 8.435e-07, size: 576, ETA: 0:02:34
2025-08-28 16:35:07.874 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 593/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.0, iou_loss: 1.8, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.6, lr: 8.105e-07, size: 576, ETA: 0:02:31
2025-08-28 16:35:10.977 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 593/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.7, lr: 7.782e-07, size: 320, ETA: 0:02:28
2025-08-28 16:35:13.992 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 593/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 7.465e-07, size: 448, ETA: 0:02:25
2025-08-28 16:35:17.087 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 593/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 7.154e-07, size: 256, ETA: 0:02:22
2025-08-28 16:35:20.340 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 593/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 6.851e-07, size: 288, ETA: 0:02:19
2025-08-28 16:35:21.615 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:35:27.709 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:35:28.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:35:29.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6021
2025-08-28 16:35:29.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5109
2025-08-28 16:35:29.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4000
2025-08-28 16:35:29.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5044
2025-08-28 16:35:29.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:35:29.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:35:29.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.602
2025-08-28 16:35:29.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-08-28 16:35:29.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-28 16:35:29.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-08-28 16:35:29.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:35:29.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:35:29.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:35:29.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:35:29.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:35:29.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:35:29.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:35:29.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:35:29.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:35:30.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:35:31.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:35:32.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:35:33.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:35:33.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:35:34.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:35:35.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:35:36.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:35:37.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:35:37.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-08-28 16:35:37.251 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-08-28 16:35:37.251 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:35:37.264 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.90 ms, Average inference time: 7.04 ms

2025-08-28 16:35:37.264 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:35:37.381 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:35:37.482 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch594
2025-08-28 16:35:40.343 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 594/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 6.422e-07, size: 544, ETA: 0:02:14
2025-08-28 16:35:43.440 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 594/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 6.134e-07, size: 384, ETA: 0:02:11
2025-08-28 16:35:46.440 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 594/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.6, lr: 5.853e-07, size: 512, ETA: 0:02:08
2025-08-28 16:35:49.577 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 594/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 5.579e-07, size: 352, ETA: 0:02:05
2025-08-28 16:35:52.742 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 594/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.8, lr: 5.311e-07, size: 320, ETA: 0:02:02
2025-08-28 16:35:55.725 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 594/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.3, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.6, lr: 5.050e-07, size: 448, ETA: 0:01:59
2025-08-28 16:35:57.096 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:36:03.255 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:36:04.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:36:04.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6095
2025-08-28 16:36:04.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5438
2025-08-28 16:36:04.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4000
2025-08-28 16:36:04.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5178
2025-08-28 16:36:04.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:36:04.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:36:04.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-08-28 16:36:04.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.544
2025-08-28 16:36:04.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-08-28 16:36:04.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.518
2025-08-28 16:36:04.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:36:04.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:36:04.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:36:04.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:36:04.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:36:04.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:36:04.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:36:04.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:36:04.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:36:05.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:36:05.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:36:06.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:36:07.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:36:07.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:36:08.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:36:09.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:36:09.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:36:10.485 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:36:10.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.28
2025-08-28 16:36:10.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:36:10.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:36:10.493 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.90 ms, Average inference time: 7.17 ms

2025-08-28 16:36:10.494 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:36:10.572 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:36:10.648 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch595
2025-08-28 16:36:13.481 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 595/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.7, lr: 4.683e-07, size: 384, ETA: 0:01:54
2025-08-28 16:36:16.528 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 595/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 4.438e-07, size: 352, ETA: 0:01:51
2025-08-28 16:36:19.624 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 595/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.5, lr: 4.199e-07, size: 448, ETA: 0:01:48
2025-08-28 16:36:22.560 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 595/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 3.967e-07, size: 352, ETA: 0:01:45
2025-08-28 16:36:25.709 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 595/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 3.742e-07, size: 352, ETA: 0:01:42
2025-08-28 16:36:28.693 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 595/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 3.523e-07, size: 288, ETA: 0:01:39
2025-08-28 16:36:29.991 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:36:36.185 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:36:37.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:36:37.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6154
2025-08-28 16:36:37.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5484
2025-08-28 16:36:37.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4109
2025-08-28 16:36:37.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5249
2025-08-28 16:36:37.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:36:37.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:36:37.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.615
2025-08-28 16:36:37.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.548
2025-08-28 16:36:37.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-08-28 16:36:37.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.525
2025-08-28 16:36:37.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:36:37.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:36:37.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:36:37.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:36:37.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:36:37.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:36:37.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:36:37.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:36:37.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:36:38.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:36:39.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:36:40.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:36:40.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:36:41.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:36:42.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:36:43.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:36:43.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:36:44.694 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:36:44.694 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:36:44.694 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:36:44.694 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:36:44.701 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.95 ms, Average inference time: 7.13 ms

2025-08-28 16:36:44.702 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:36:44.833 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:36:44.905 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch596
2025-08-28 16:36:47.889 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 596/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 3.218e-07, size: 416, ETA: 0:01:35
2025-08-28 16:36:50.945 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 596/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 3.015e-07, size: 480, ETA: 0:01:32
2025-08-28 16:36:53.872 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 596/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 2.819e-07, size: 416, ETA: 0:01:29
2025-08-28 16:36:56.854 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 596/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.7, lr: 2.629e-07, size: 448, ETA: 0:01:26
2025-08-28 16:36:59.998 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 596/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 2.447e-07, size: 512, ETA: 0:01:23
2025-08-28 16:37:03.172 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 596/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 2.270e-07, size: 416, ETA: 0:01:20
2025-08-28 16:37:04.547 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:37:10.701 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:37:11.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:37:12.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6157
2025-08-28 16:37:12.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5414
2025-08-28 16:37:12.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4263
2025-08-28 16:37:12.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5278
2025-08-28 16:37:12.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:37:12.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:37:12.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.616
2025-08-28 16:37:12.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-08-28 16:37:12.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-08-28 16:37:12.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.528
2025-08-28 16:37:12.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:37:12.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:37:12.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:37:12.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:37:12.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:37:12.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:37:12.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:37:12.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:37:12.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:37:12.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:37:13.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:37:14.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:37:15.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:37:15.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:37:16.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:37:17.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:37:17.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:37:18.476 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:37:18.476 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-08-28 16:37:18.476 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.53
2025-08-28 16:37:18.476 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:37:18.484 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.93 ms, Average inference time: 7.05 ms

2025-08-28 16:37:18.485 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:37:18.569 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:37:18.651 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch597
2025-08-28 16:37:21.511 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 597/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.7, lr: 2.026e-07, size: 512, ETA: 0:01:15
2025-08-28 16:37:24.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 597/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 1.866e-07, size: 416, ETA: 0:01:12
2025-08-28 16:37:27.702 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 597/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 1.713e-07, size: 320, ETA: 0:01:09
2025-08-28 16:37:30.645 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 597/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 3.1, iou_loss: 1.2, l1_loss: 0.4, conf_loss: 1.0, cls_loss: 0.4, lr: 1.566e-07, size: 480, ETA: 0:01:06
2025-08-28 16:37:33.666 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 597/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.8, lr: 1.425e-07, size: 576, ETA: 0:01:03
2025-08-28 16:37:36.820 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 597/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.7, lr: 1.292e-07, size: 480, ETA: 0:01:00
2025-08-28 16:37:38.178 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:37:44.584 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:37:45.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:37:46.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6134
2025-08-28 16:37:46.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5360
2025-08-28 16:37:46.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4089
2025-08-28 16:37:46.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5194
2025-08-28 16:37:46.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:37:46.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:37:46.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.613
2025-08-28 16:37:46.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-08-28 16:37:46.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-08-28 16:37:46.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.519
2025-08-28 16:37:46.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:37:46.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:37:46.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:37:46.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:37:46.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:37:46.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:37:46.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:37:46.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:37:46.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:37:47.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:37:47.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:37:48.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:37:49.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:37:50.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:37:51.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:37:51.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:37:52.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:37:53.457 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:37:53.457 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-08-28 16:37:53.457 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:37:53.457 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:37:53.466 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.33 ms, Average NMS time: 0.92 ms, Average inference time: 7.25 ms

2025-08-28 16:37:53.467 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:37:53.590 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:37:53.662 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch598
2025-08-28 16:37:56.691 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 598/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.109e-07, size: 480, ETA: 0:00:55
2025-08-28 16:37:59.832 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 598/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 9.918e-08, size: 512, ETA: 0:00:52
2025-08-28 16:38:02.874 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 598/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 8.808e-08, size: 320, ETA: 0:00:49
2025-08-28 16:38:05.926 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 598/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 7.764e-08, size: 320, ETA: 0:00:46
2025-08-28 16:38:08.800 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 598/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 3.0, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 0.7, cls_loss: 0.5, lr: 6.785e-08, size: 416, ETA: 0:00:43
2025-08-28 16:38:11.857 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 598/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 5.872e-08, size: 576, ETA: 0:00:40
2025-08-28 16:38:13.280 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:38:19.328 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:38:20.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:38:20.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6138
2025-08-28 16:38:20.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5438
2025-08-28 16:38:20.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4137
2025-08-28 16:38:20.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5237
2025-08-28 16:38:20.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:38:20.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:38:20.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.614
2025-08-28 16:38:20.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.544
2025-08-28 16:38:20.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-08-28 16:38:20.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.524
2025-08-28 16:38:20.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:38:20.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:38:20.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:38:20.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:38:20.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:38:20.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:38:20.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:38:20.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:38:20.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:38:21.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:38:21.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:38:22.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:38:23.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:38:23.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:38:24.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:38:25.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:38:25.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:38:26.287 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:38:26.288 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.28
2025-08-28 16:38:26.288 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-08-28 16:38:26.288 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:38:26.295 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.90 ms, Average inference time: 7.12 ms

2025-08-28 16:38:26.296 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:38:26.379 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:38:26.460 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch599
2025-08-28 16:38:29.335 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 599/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.7, lr: 4.666e-08, size: 512, ETA: 0:00:36
2025-08-28 16:38:32.466 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 599/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.8, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.7, lr: 3.915e-08, size: 352, ETA: 0:00:33
2025-08-28 16:38:35.381 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 599/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 3.229e-08, size: 448, ETA: 0:00:30
2025-08-28 16:38:38.693 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 599/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 2.610e-08, size: 576, ETA: 0:00:27
2025-08-28 16:38:41.839 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 599/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 2.056e-08, size: 384, ETA: 0:00:24
2025-08-28 16:38:44.797 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 599/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.8, lr: 1.569e-08, size: 384, ETA: 0:00:21
2025-08-28 16:38:46.173 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:38:52.487 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:38:53.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:38:53.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6210
2025-08-28 16:38:53.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5477
2025-08-28 16:38:53.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4249
2025-08-28 16:38:53.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5312
2025-08-28 16:38:53.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:38:53.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:38:53.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.621
2025-08-28 16:38:53.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.548
2025-08-28 16:38:53.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.425
2025-08-28 16:38:53.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.531
2025-08-28 16:38:53.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:38:53.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:38:53.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:38:53.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:38:53.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:38:53.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:38:53.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:38:53.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:38:53.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:38:54.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:38:55.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:38:55.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:38:56.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:38:57.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:38:57.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:38:58.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:38:59.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:38:59.903 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:38:59.904 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.29
2025-08-28 16:38:59.904 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.53
2025-08-28 16:38:59.904 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:38:59.911 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.38 ms, Average NMS time: 0.94 ms, Average inference time: 7.32 ms

2025-08-28 16:38:59.912 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:39:00.000 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:39:00.081 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch600
2025-08-28 16:39:03.021 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 600/600, iter: 20/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 9.787e-09, size: 288, ETA: 0:00:16
2025-08-28 16:39:06.048 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 600/600, iter: 40/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 0.6, cls_loss: 0.5, lr: 6.525e-09, size: 288, ETA: 0:00:13
2025-08-28 16:39:08.958 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 600/600, iter: 60/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.7, lr: 3.922e-09, size: 448, ETA: 0:00:10
2025-08-28 16:39:11.945 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 600/600, iter: 80/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 1.978e-09, size: 352, ETA: 0:00:07
2025-08-28 16:39:14.932 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 600/600, iter: 100/129, gpu mem: 1367Mb, mem: 45.1Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 6.928e-10, size: 512, ETA: 0:00:04
2025-08-28 16:39:17.985 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 600/600, iter: 120/129, gpu mem: 1367Mb, mem: 45.2Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 6.672e-11, size: 576, ETA: 0:00:01
2025-08-28 16:39:19.401 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:39:25.642 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-08-28 16:39:26.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-08-28 16:39:27.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6206
2025-08-28 16:39:27.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5454
2025-08-28 16:39:27.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4260
2025-08-28 16:39:27.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5307
2025-08-28 16:39:27.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-08-28 16:39:27.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-08-28 16:39:27.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.621
2025-08-28 16:39:27.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-08-28 16:39:27.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-08-28 16:39:27.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.531
2025-08-28 16:39:27.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-08-28 16:39:27.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-08-28 16:39:27.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-08-28 16:39:27.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-08-28 16:39:27.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-08-28 16:39:27.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-08-28 16:39:27.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-08-28 16:39:27.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-08-28 16:39:27.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-08-28 16:39:27.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-08-28 16:39:28.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-08-28 16:39:29.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-08-28 16:39:29.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-08-28 16:39:30.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-08-28 16:39:31.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-08-28 16:39:32.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-08-28 16:39:32.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-08-28 16:39:33.414 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-08-28 16:39:33.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.29
2025-08-28 16:39:33.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.53
2025-08-28 16:39:33.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-08-28 16:39:33.422 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.93 ms, Average inference time: 7.09 ms

2025-08-28 16:39:33.423 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:39:33.503 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_190k_trainset_fusebn_ranger
2025-08-28 16:39:33.580 | INFO     | yolox_microbt.core.trainer:after_train:172 - Training of experiment is done and the best AP is 29.77
