2025-09-01 10:56:48.708 | INFO     | yolox_microbt.core.trainer:before_train:88 - args: Namespace(config='configs.sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger', experiment_name='sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_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=[])
2025-09-01 10:56:48.711 | 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_185k_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                                                                  │
╘═══════════════════╧════════════════════════════════════════════════════════════════════════╛
2025-09-01 10:56:49.559 | INFO     | yolox_microbt.core.trainer:before_train:129 - init prefetcher, this might take one minute or less...
2025-09-01 10:56:52.882 | INFO     | yolox_microbt.core.trainer:before_train:168 - Training start...
2025-09-01 10:56:53.020 | 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, 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)
                )
              )
            )
          )
          (2): 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, 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, 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)
                )
              )
            )
            (1): Module(
              (conv_pw): ConvReLU2d(
                32, 128, 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(
                128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128
                (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(
                128, 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)
                )
              )
            )
            (2): Module(
              (conv_pw): ConvReLU2d(
                32, 128, 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(
                128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128
                (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(
                128, 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)
                )
              )
            )
          )
          (5): Module(
            (0): Module(
              (conv_pw): ConvReLU2d(
                32, 128, 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(
                128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128
                (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(
                128, 38, 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(
                38, 152, 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(
                152, 152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=152
                (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(
                152, 42, 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(
                42, 168, 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(
                168, 168, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=168
                (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(
                168, 80, 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(
                80, 320, 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(
                320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=320
                (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(
                320, 80, 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(
                80, 320, 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(
                320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=320
                (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(
                320, 128, 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(
              42, 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(
              128, 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)
      )
      (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_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_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_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_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_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_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_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_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_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_5_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()
    )
  )
)
2025-09-01 10:56:53.021 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch1
2025-09-01 10:56:53.037 | INFO     | yolox_microbt.core.trainer:before_epoch:200 - --->No mosaic aug for calibration model!
2025-09-01 10:56:56.862 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 1/600, iter: 20/129, gpu mem: 544Mb, mem: 45.3Gb, iter_time: 0.190s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 2.000e-03, size: 288, ETA: 4:05:21
2025-09-01 10:56:59.896 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 1/600, iter: 40/129, gpu mem: 674Mb, mem: 45.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.7, l1_loss: 0.0, conf_loss: 1.2, cls_loss: 0.5, lr: 2.000e-03, size: 480, ETA: 3:39:37
2025-09-01 10:57:02.901 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 1/600, iter: 60/129, gpu mem: 1252Mb, mem: 45.3Gb, iter_time: 0.149s, 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: 2.000e-03, size: 256, ETA: 3:30:20
2025-09-01 10:57:05.988 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 1/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 1.8, cls_loss: 0.6, lr: 2.000e-03, size: 576, ETA: 3:27:05
2025-09-01 10:57:08.795 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 1/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.3Gb, iter_time: 0.139s, 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: 2.000e-03, size: 288, ETA: 3:21:32
2025-09-01 10:57:11.448 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 1/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.3Gb, iter_time: 0.132s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 1.7, cls_loss: 0.8, lr: 2.000e-03, size: 288, ETA: 3:16:08
2025-09-01 10:57:12.660 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 10:57:18.954 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 10:57:20.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 10:57:20.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6353
2025-09-01 10:57:20.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5757
2025-09-01 10:57:20.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4261
2025-09-01 10:57:20.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5457
2025-09-01 10:57:20.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 10:57:20.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 10:57:20.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.635
2025-09-01 10:57:20.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-09-01 10:57:20.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-09-01 10:57:20.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.546
2025-09-01 10:57:20.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 10:57:20.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 10:57:20.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 10:57:20.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 10:57:20.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 10:57:20.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 10:57:20.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 10:57:20.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 10:57:20.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 10:57:21.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 10:57:22.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 10:57:23.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 10:57:23.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 10:57:24.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 10:57:25.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 10:57:25.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 10:57:26.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 10:57:27.045 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 10:57:27.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.31
2025-09-01 10:57:27.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.55
2025-09-01 10:57:27.046 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 10:57:27.053 | 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

2025-09-01 10:57:27.054 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 10:57:27.146 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 10:57:27.225 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch2
2025-09-01 10:57:27.230 | INFO     | yolox_microbt.core.trainer:before_epoch:200 - --->No mosaic aug for calibration model!
2025-09-01 10:57:29.776 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 2/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.3Gb, iter_time: 0.126s, data_time: 0.002s, total_loss: 3.3, iou_loss: 1.7, l1_loss: 0.0, conf_loss: 1.1, cls_loss: 0.5, lr: 2.000e-03, size: 352, ETA: 3:10:04
2025-09-01 10:57:32.415 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 2/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.4Gb, iter_time: 0.130s, data_time: 0.002s, total_loss: 4.0, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 1.2, cls_loss: 0.6, lr: 2.000e-03, size: 576, ETA: 3:07:23
2025-09-01 10:57:34.941 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 2/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.3Gb, iter_time: 0.125s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.5, l1_loss: 0.0, conf_loss: 1.6, cls_loss: 0.5, lr: 2.000e-03, size: 544, ETA: 3:04:29
2025-09-01 10:57:37.389 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 2/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.4Gb, iter_time: 0.122s, data_time: 0.002s, total_loss: 2.8, iou_loss: 1.4, l1_loss: 0.0, conf_loss: 1.0, cls_loss: 0.5, lr: 2.000e-03, size: 320, ETA: 3:01:46
2025-09-01 10:57:40.216 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 2/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.4Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 3.0, iou_loss: 1.6, l1_loss: 0.0, conf_loss: 1.0, cls_loss: 0.5, lr: 2.000e-03, size: 384, ETA: 3:01:37
2025-09-01 10:57:42.753 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 2/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.3Gb, iter_time: 0.125s, data_time: 0.002s, total_loss: 2.7, iou_loss: 1.3, l1_loss: 0.0, conf_loss: 1.0, cls_loss: 0.5, lr: 2.000e-03, size: 448, ETA: 2:59:56
2025-09-01 10:57:43.953 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 10:57:50.021 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 10:57:50.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 10:57:51.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6379
2025-09-01 10:57:51.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5762
2025-09-01 10:57:51.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3988
2025-09-01 10:57:51.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5376
2025-09-01 10:57:51.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 10:57:51.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 10:57:51.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.638
2025-09-01 10:57:51.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-09-01 10:57:51.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-09-01 10:57:51.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.538
2025-09-01 10:57:51.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 10:57:51.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 10:57:51.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 10:57:51.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 10:57:51.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 10:57:51.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 10:57:51.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 10:57:51.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 10:57:51.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 10:57:51.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 10:57:52.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 10:57:52.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 10:57:53.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 10:57:53.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 10:57:54.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 10:57:54.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 10:57:55.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 10:57:55.785 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 10:57:55.785 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.31
2025-09-01 10:57:55.785 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.54
2025-09-01 10:57:55.785 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 10:57:55.792 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.87 ms, Average inference time: 6.97 ms

2025-09-01 10:57:55.793 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 10:57:55.865 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 10:57:55.946 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch3
2025-09-01 10:57:55.996 | INFO     | yolox_microbt.core.trainer:before_epoch:204 - --->enable mosaic aug for quantization training!
2025-09-01 10:57:59.137 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 3/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 9.4, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 4.8, cls_loss: 1.0, lr: 2.000e-03, size: 576, ETA: 3:00:52
2025-09-01 10:58:02.646 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 3/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.4Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 2.000e-03, size: 288, ETA: 3:03:40
2025-09-01 10:58:05.836 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 3/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 2.000e-03, size: 448, ETA: 3:04:43
2025-09-01 10:58:09.058 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 3/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 2.000e-03, size: 256, ETA: 3:05:46
2025-09-01 10:58:12.245 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 3/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.4Gb, iter_time: 0.157s, 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: 2.000e-03, size: 320, ETA: 3:06:34
2025-09-01 10:58:15.451 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 3/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.4Gb, 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: 0.8, lr: 2.000e-03, size: 288, ETA: 3:07:22
2025-09-01 10:58:16.997 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 10:58:23.362 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 10:58:28.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 10:58:31.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4960
2025-09-01 10:58:31.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4619
2025-09-01 10:58:31.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2156
2025-09-01 10:58:31.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3912
2025-09-01 10:58:31.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 10:58:31.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 10:58:31.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-09-01 10:58:31.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-09-01 10:58:31.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.216
2025-09-01 10:58:31.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.391
2025-09-01 10:58:31.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 10:58:31.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 10:58:31.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 10:58:31.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 10:58:31.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 10:58:31.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 10:58:31.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 10:58:31.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 10:58:31.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 10:58:35.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 10:58:38.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 10:58:42.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 10:58:45.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 10:58:48.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 10:58:52.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 10:58:55.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 10:58:59.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 10:59:02.407 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 10:59:02.407 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-09-01 10:59:02.408 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-09-01 10:59:02.408 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 10:59:02.434 | 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

2025-09-01 10:59:02.436 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 10:59:02.530 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 10:59:02.630 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch4
2025-09-01 10:59:05.775 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 4/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.155s, 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: 2.000e-03, size: 512, ETA: 3:08:31
2025-09-01 10:59:08.961 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 4/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 2.000e-03, size: 416, ETA: 3:09:04
2025-09-01 10:59:12.302 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 4/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, 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: 2.000e-03, size: 448, ETA: 3:09:59
2025-09-01 10:59:15.547 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 4/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.160s, 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: 2.000e-03, size: 512, ETA: 3:10:34
2025-09-01 10:59:18.772 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 4/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 2.000e-03, size: 352, ETA: 3:11:03
2025-09-01 10:59:22.169 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 4/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.168s, 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: 2.000e-03, size: 576, ETA: 3:11:56
2025-09-01 10:59:23.681 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 10:59:29.917 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 10:59:32.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 10:59:33.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5571
2025-09-01 10:59:33.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5060
2025-09-01 10:59:33.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3019
2025-09-01 10:59:33.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4550
2025-09-01 10:59:33.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 10:59:33.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 10:59:33.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.557
2025-09-01 10:59:33.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-09-01 10:59:33.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.302
2025-09-01 10:59:33.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.455
2025-09-01 10:59:33.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 10:59:33.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 10:59:33.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 10:59:33.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 10:59:33.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 10:59:33.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 10:59:33.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 10:59:33.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 10:59:33.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 10:59:35.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 10:59:37.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 10:59:38.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 10:59:40.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 10:59:42.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 10:59:44.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 10:59:45.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 10:59:47.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 10:59:49.217 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 10:59:49.218 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 10:59:49.218 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 10:59:49.218 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 10:59:49.249 | 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

2025-09-01 10:59:49.250 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 10:59:49.376 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 10:59:49.527 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch5
2025-09-01 10:59:52.531 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 5/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 2.000e-03, size: 416, ETA: 3:12:08
2025-09-01 10:59:55.811 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 5/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 2.000e-03, size: 544, ETA: 3:12:37
2025-09-01 10:59:58.958 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 5/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 2.000e-03, size: 416, ETA: 3:12:45
2025-09-01 11:00:02.277 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 5/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 0.9, lr: 2.000e-03, size: 256, ETA: 3:13:16
2025-09-01 11:00:05.735 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 5/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 2.000e-03, size: 256, ETA: 3:14:01
2025-09-01 11:00:09.030 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 5/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.163s, 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: 2.000e-03, size: 384, ETA: 3:14:26
2025-09-01 11:00:10.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:00:16.696 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:00:20.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:00:22.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5727
2025-09-01 11:00:22.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5105
2025-09-01 11:00:22.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2946
2025-09-01 11:00:22.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4593
2025-09-01 11:00:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:00:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:00:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.573
2025-09-01 11:00:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-09-01 11:00:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-09-01 11:00:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.459
2025-09-01 11:00:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:00:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:00:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:00:22.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:00:22.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:00:22.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:00:22.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:00:22.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:00:22.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:00:25.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:00:28.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:00:31.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:00:34.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:00:37.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:00:39.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:00:42.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:00:45.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:00:48.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:00:48.402 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 11:00:48.403 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 11:00:48.403 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:00:48.429 | 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

2025-09-01 11:00:48.442 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:00:48.522 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:00:48.608 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch6
2025-09-01 11:00:51.662 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 6/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.150s, 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: 2.000e-03, size: 384, ETA: 3:14:33
2025-09-01 11:00:55.037 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 6/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 2.000e-03, size: 256, ETA: 3:15:03
2025-09-01 11:00:58.194 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 6/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 2.000e-03, size: 320, ETA: 3:15:04
2025-09-01 11:01:01.513 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 6/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, 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: 2.000e-03, size: 512, ETA: 3:15:25
2025-09-01 11:01:04.971 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 6/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 2.000e-03, size: 480, ETA: 3:15:59
2025-09-01 11:01:08.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 6/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 2.000e-03, size: 320, ETA: 3:16:13
2025-09-01 11:01:09.725 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:01:15.994 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:01:18.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:01:20.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5883
2025-09-01 11:01:20.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5330
2025-09-01 11:01:21.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3034
2025-09-01 11:01:21.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4749
2025-09-01 11:01:21.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:01:21.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:01:21.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-09-01 11:01:21.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-09-01 11:01:21.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-09-01 11:01:21.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.475
2025-09-01 11:01:21.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:01:21.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:01:21.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:01:21.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:01:21.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:01:21.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:01:21.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:01:21.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:01:21.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:01:23.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:01:25.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:01:27.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:01:29.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:01:31.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:01:33.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:01:35.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:01:38.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:01:40.396 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:01:40.397 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 11:01:40.397 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 11:01:40.397 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:01:40.425 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.41 ms, Average NMS time: 0.95 ms, Average inference time: 7.36 ms

2025-09-01 11:01:40.426 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:01:40.549 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:01:40.644 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch7
2025-09-01 11:01:43.792 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 7/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.156s, 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: 1.999e-03, size: 352, ETA: 3:16:19
2025-09-01 11:01:46.869 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 7/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.152s, 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: 1.999e-03, size: 384, ETA: 3:16:12
2025-09-01 11:01:50.129 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 7/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.999e-03, size: 352, ETA: 3:16:22
2025-09-01 11:01:53.482 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 7/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.165s, 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: 1.999e-03, size: 416, ETA: 3:16:38
2025-09-01 11:01:56.750 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 7/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.999e-03, size: 544, ETA: 3:16:47
2025-09-01 11:02:00.087 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 7/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.999e-03, size: 416, ETA: 3:17:03
2025-09-01 11:02:01.548 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:02:07.898 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:02:10.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:02:12.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5637
2025-09-01 11:02:12.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5248
2025-09-01 11:02:12.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2945
2025-09-01 11:02:12.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4610
2025-09-01 11:02:12.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:02:12.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:02:12.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.564
2025-09-01 11:02:12.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-09-01 11:02:12.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-09-01 11:02:12.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.461
2025-09-01 11:02:12.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:02:12.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:02:12.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:02:12.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:02:12.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:02:12.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:02:12.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:02:12.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:02:12.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:02:15.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:02:17.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:02:19.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:02:21.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:02:24.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:02:26.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:02:28.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:02:30.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:02:33.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:02:33.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 11:02:33.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 11:02:33.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:02:33.035 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.35 ms, Average NMS time: 0.94 ms, Average inference time: 7.28 ms

2025-09-01 11:02:33.037 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:02:33.167 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:02:33.263 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch8
2025-09-01 11:02:36.413 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 8/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.999e-03, size: 576, ETA: 3:17:04
2025-09-01 11:02:39.711 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 8/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.999e-03, size: 544, ETA: 3:17:14
2025-09-01 11:02:42.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 8/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.162s, 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: 1.999e-03, size: 256, ETA: 3:17:22
2025-09-01 11:02:46.240 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 8/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.999e-03, size: 256, ETA: 3:17:27
2025-09-01 11:02:49.598 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 8/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.999e-03, size: 416, ETA: 3:17:39
2025-09-01 11:02:52.894 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 8/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.999e-03, size: 352, ETA: 3:17:48
2025-09-01 11:02:54.280 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:03:00.800 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:03:03.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:03:05.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5805
2025-09-01 11:03:05.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5439
2025-09-01 11:03:05.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3196
2025-09-01 11:03:05.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4813
2025-09-01 11:03:05.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:03:05.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:03:05.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.580
2025-09-01 11:03:05.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.544
2025-09-01 11:03:05.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.320
2025-09-01 11:03:05.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-09-01 11:03:05.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:03:05.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:03:05.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:03:05.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:03:05.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:03:05.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:03:05.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:03:05.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:03:05.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:03:08.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:03:10.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:03:12.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:03:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:03:16.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:03:19.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:03:21.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:03:23.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:03:25.840 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:03:25.841 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 11:03:25.841 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 11:03:25.841 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:03:25.871 | 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: 7.24 ms

2025-09-01 11:03:25.872 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:03:25.958 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:03:26.047 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch9
2025-09-01 11:03:29.184 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 9/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.2, lr: 1.999e-03, size: 416, ETA: 3:17:41
2025-09-01 11:03:32.436 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 9/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.2, lr: 1.999e-03, size: 448, ETA: 3:17:45
2025-09-01 11:03:35.674 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 9/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.999e-03, size: 448, ETA: 3:17:47
2025-09-01 11:03:38.912 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 9/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.999e-03, size: 512, ETA: 3:17:52
2025-09-01 11:03:42.166 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 9/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.999e-03, size: 416, ETA: 3:17:55
2025-09-01 11:03:45.454 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 9/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.161s, 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: 1.999e-03, size: 256, ETA: 3:17:59
2025-09-01 11:03:46.831 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:03:52.995 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:03:56.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:03:58.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5849
2025-09-01 11:03:58.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4907
2025-09-01 11:03:58.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3240
2025-09-01 11:03:58.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4666
2025-09-01 11:03:58.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:03:58.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:03:58.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.585
2025-09-01 11:03:58.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-09-01 11:03:58.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.324
2025-09-01 11:03:58.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.467
2025-09-01 11:03:58.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:03:58.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:03:58.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:03:58.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:03:58.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:03:58.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:03:58.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:03:58.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:03:58.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:04:01.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:04:04.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:04:07.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:04:09.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:04:12.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:04:14.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:04:17.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:04:20.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:04:23.146 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:04:23.147 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 11:04:23.147 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 11:04:23.147 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:04:23.173 | 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

2025-09-01 11:04:23.173 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:04:23.322 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:04:23.420 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch10
2025-09-01 11:04:26.515 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 10/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.999e-03, size: 576, ETA: 3:17:49
2025-09-01 11:04:29.839 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 10/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.999e-03, size: 352, ETA: 3:17:56
2025-09-01 11:04:33.055 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 10/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.999e-03, size: 384, ETA: 3:17:58
2025-09-01 11:04:36.264 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 10/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.157s, 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: 1.999e-03, size: 256, ETA: 3:17:56
2025-09-01 11:04:39.347 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 10/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.7, lr: 1.999e-03, size: 416, ETA: 3:17:49
2025-09-01 11:04:42.727 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 10/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.168s, 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: 1.999e-03, size: 544, ETA: 3:18:00
2025-09-01 11:04:44.246 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:04:50.371 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:04:52.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:04:53.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5721
2025-09-01 11:04:53.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5295
2025-09-01 11:04:53.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3054
2025-09-01 11:04:53.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4690
2025-09-01 11:04:53.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:04:53.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:04:53.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-09-01 11:04:53.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-09-01 11:04:53.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.305
2025-09-01 11:04:53.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.469
2025-09-01 11:04:53.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:04:53.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:04:53.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:04:53.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:04:53.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:04:53.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:04:53.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:04:53.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:04:53.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:04:55.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:04:56.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:04:58.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:04:59.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:05:01.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:05:02.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:05:04.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:05:05.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:05:07.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:05:07.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 11:05:07.220 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 11:05:07.220 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:05:07.230 | 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

2025-09-01 11:05:07.232 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:05:07.315 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:05:07.400 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch11
2025-09-01 11:05:10.586 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 11/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.999e-03, size: 352, ETA: 3:18:02
2025-09-01 11:05:13.753 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 11/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.156s, 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: 1.999e-03, size: 384, ETA: 3:17:59
2025-09-01 11:05:16.934 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 11/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.156s, 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: 1.998e-03, size: 352, ETA: 3:17:56
2025-09-01 11:05:20.192 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 11/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, 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: 1.998e-03, size: 544, ETA: 3:17:58
2025-09-01 11:05:23.521 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 11/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.998e-03, size: 576, ETA: 3:18:04
2025-09-01 11:05:26.860 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 11/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.998e-03, size: 352, ETA: 3:18:10
2025-09-01 11:05:28.369 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:05:34.656 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:05:36.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:05:38.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5578
2025-09-01 11:05:38.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5172
2025-09-01 11:05:38.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3033
2025-09-01 11:05:38.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4594
2025-09-01 11:05:38.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:05:38.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:05:38.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.558
2025-09-01 11:05:38.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 11:05:38.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-09-01 11:05:38.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.459
2025-09-01 11:05:38.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:05:38.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:05:38.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:05:38.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:05:38.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:05:38.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:05:38.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:05:38.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:05:38.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:05:40.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:05:41.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:05:43.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:05:45.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:05:46.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:05:48.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:05:50.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:05:51.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:05:53.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:05:53.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:05:53.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 11:05:53.604 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:05:53.637 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.38 ms, Average NMS time: 0.98 ms, Average inference time: 7.36 ms

2025-09-01 11:05:53.638 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:05:53.811 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:05:53.909 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch12
2025-09-01 11:05:57.056 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 12/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.998e-03, size: 448, ETA: 3:18:09
2025-09-01 11:06:00.253 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 12/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.1, lr: 1.998e-03, size: 256, ETA: 3:18:06
2025-09-01 11:06:03.434 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 12/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.158s, 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: 1.998e-03, size: 320, ETA: 3:18:05
2025-09-01 11:06:06.715 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 12/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.6, lr: 1.998e-03, size: 512, ETA: 3:18:09
2025-09-01 11:06:10.054 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 12/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.998e-03, size: 352, ETA: 3:18:13
2025-09-01 11:06:13.394 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 12/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.998e-03, size: 544, ETA: 3:18:18
2025-09-01 11:06:14.817 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:06:21.038 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:06:23.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:06:24.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5758
2025-09-01 11:06:24.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5292
2025-09-01 11:06:24.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3125
2025-09-01 11:06:24.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4725
2025-09-01 11:06:24.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:06:24.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:06:24.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-09-01 11:06:24.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-09-01 11:06:24.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.313
2025-09-01 11:06:24.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.473
2025-09-01 11:06:24.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:06:24.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:06:24.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:06:24.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:06:24.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:06:24.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:06:24.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:06:24.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:06:24.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:06:26.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:06:28.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:06:30.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:06:32.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:06:33.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:06:35.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:06:37.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:06:39.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:06:40.845 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:06:40.845 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 11:06:40.845 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 11:06:40.845 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:06:40.873 | 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

2025-09-01 11:06:40.877 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:06:40.961 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:06:41.046 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch13
2025-09-01 11:06:44.132 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 13/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.153s, 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: 1.998e-03, size: 512, ETA: 3:18:10
2025-09-01 11:06:47.440 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 13/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.998e-03, size: 576, ETA: 3:18:12
2025-09-01 11:06:50.750 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 13/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.998e-03, size: 480, ETA: 3:18:16
2025-09-01 11:06:53.986 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 13/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.160s, 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: 1.998e-03, size: 512, ETA: 3:18:16
2025-09-01 11:06:57.378 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 13/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.168s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.998e-03, size: 416, ETA: 3:18:23
2025-09-01 11:07:00.576 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 13/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.158s, 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: 320, ETA: 3:18:21
2025-09-01 11:07:01.944 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:07:08.146 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:07:10.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:07:12.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5657
2025-09-01 11:07:12.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5024
2025-09-01 11:07:12.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3343
2025-09-01 11:07:12.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4675
2025-09-01 11:07:12.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:07:12.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:07:12.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.566
2025-09-01 11:07:12.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-09-01 11:07:12.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-09-01 11:07:12.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.467
2025-09-01 11:07:12.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:07:12.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:07:12.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:07:12.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:07:12.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:07:12.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:07:12.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:07:12.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:07:12.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:07:14.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:07:16.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:07:18.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:07:20.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:07:22.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:07:24.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:07:26.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:07:28.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:07:30.221 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:07:30.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 11:07:30.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 11:07:30.222 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:07:30.250 | 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

2025-09-01 11:07:30.251 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:07:30.339 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:07:30.424 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch14
2025-09-01 11:07:33.521 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 14/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.998e-03, size: 512, ETA: 3:18:10
2025-09-01 11:07:36.856 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 14/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.998e-03, size: 576, ETA: 3:18:14
2025-09-01 11:07:40.078 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 14/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.998e-03, size: 480, ETA: 3:18:12
2025-09-01 11:07:43.547 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 14/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.997e-03, size: 352, ETA: 3:18:21
2025-09-01 11:07:46.710 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 14/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.156s, 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: 1.997e-03, size: 320, ETA: 3:18:16
2025-09-01 11:07:49.906 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 14/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.159s, 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: 1.997e-03, size: 576, ETA: 3:18:14
2025-09-01 11:07:51.439 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:07:57.801 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:07:59.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:08:01.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5410
2025-09-01 11:08:01.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5145
2025-09-01 11:08:01.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2358
2025-09-01 11:08:01.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4304
2025-09-01 11:08:01.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:08:01.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:08:01.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-09-01 11:08:01.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.514
2025-09-01 11:08:01.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.236
2025-09-01 11:08:01.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.430
2025-09-01 11:08:01.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:08:01.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:08:01.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:08:01.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:08:01.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:08:01.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:08:01.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:08:01.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:08:01.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:08:03.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:08:04.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:08:06.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:08:07.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:08:09.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:08:11.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:08:12.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:08:14.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:08:15.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:08:15.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 11:08:15.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 11:08:15.977 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:08:16.003 | 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: 7.25 ms

2025-09-01 11:08:16.004 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:08:16.093 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:08:16.196 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch15
2025-09-01 11:08:19.387 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 15/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, 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: 1.997e-03, size: 320, ETA: 3:18:14
2025-09-01 11:08:22.629 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 15/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.9, lr: 1.997e-03, size: 576, ETA: 3:18:13
2025-09-01 11:08:25.889 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 15/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, 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: 0.8, lr: 1.997e-03, size: 352, ETA: 3:18:13
2025-09-01 11:08:29.225 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 15/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.164s, 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: 1.997e-03, size: 512, ETA: 3:18:15
2025-09-01 11:08:32.518 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 15/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.997e-03, size: 384, ETA: 3:18:15
2025-09-01 11:08:35.753 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 15/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.997e-03, size: 448, ETA: 3:18:14
2025-09-01 11:08:37.158 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:08:43.582 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:08:45.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:08:46.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4974
2025-09-01 11:08:46.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4683
2025-09-01 11:08:46.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3314
2025-09-01 11:08:46.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4323
2025-09-01 11:08:46.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:08:46.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:08:46.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-09-01 11:08:46.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-09-01 11:08:46.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.331
2025-09-01 11:08:46.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.432
2025-09-01 11:08:46.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:08:46.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:08:46.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:08:46.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:08:46.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:08:46.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:08:46.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:08:46.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:08:46.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:08:48.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:08:49.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:08:51.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:08:52.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:08:54.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:08:56.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:08:57.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:08:59.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:09:00.881 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:09:00.881 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:09:00.882 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 11:09:00.882 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:09:00.908 | 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

2025-09-01 11:09:00.910 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:09:01.008 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:09:01.155 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch16
2025-09-01 11:09:04.326 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 16/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.997e-03, size: 384, ETA: 3:18:08
2025-09-01 11:09:07.641 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 16/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.997e-03, size: 544, ETA: 3:18:08
2025-09-01 11:09:10.819 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 16/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.997e-03, size: 288, ETA: 3:18:04
2025-09-01 11:09:14.077 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 16/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.997e-03, size: 512, ETA: 3:18:03
2025-09-01 11:09:17.288 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 16/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.158s, 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: 1.997e-03, size: 544, ETA: 3:18:00
2025-09-01 11:09:20.511 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 16/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.997e-03, size: 448, ETA: 3:17:58
2025-09-01 11:09:21.971 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:09:28.121 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:09:30.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:09:32.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5595
2025-09-01 11:09:32.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5079
2025-09-01 11:09:32.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3099
2025-09-01 11:09:32.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4591
2025-09-01 11:09:32.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:09:32.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:09:32.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-09-01 11:09:32.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.508
2025-09-01 11:09:32.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.310
2025-09-01 11:09:32.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.459
2025-09-01 11:09:32.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:09:32.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:09:32.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:09:32.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:09:32.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:09:32.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:09:32.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:09:32.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:09:32.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:09:34.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:09:36.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:09:38.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:09:40.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:09:42.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:09:44.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:09:46.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:09:48.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:09:50.104 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:09:50.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 11:09:50.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 11:09:50.106 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:09:50.146 | 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

2025-09-01 11:09:50.147 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:09:50.266 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:09:50.358 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch17
2025-09-01 11:09:53.506 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 17/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.155s, 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: 1.996e-03, size: 288, ETA: 3:17:52
2025-09-01 11:09:56.669 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 17/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.996e-03, size: 352, ETA: 3:17:48
2025-09-01 11:09:59.897 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 17/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.996e-03, size: 288, ETA: 3:17:46
2025-09-01 11:10:03.034 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 17/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.154s, 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: 1.996e-03, size: 288, ETA: 3:17:41
2025-09-01 11:10:06.259 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 17/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.1, lr: 1.996e-03, size: 544, ETA: 3:17:39
2025-09-01 11:10:09.700 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 17/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.170s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.996e-03, size: 320, ETA: 3:17:44
2025-09-01 11:10:11.043 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:10:17.338 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:10:20.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:10:21.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5450
2025-09-01 11:10:22.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5052
2025-09-01 11:10:22.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2977
2025-09-01 11:10:22.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4493
2025-09-01 11:10:22.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:10:22.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:10:22.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-09-01 11:10:22.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-09-01 11:10:22.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.298
2025-09-01 11:10:22.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.449
2025-09-01 11:10:22.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:10:22.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:10:22.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:10:22.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:10:22.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:10:22.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:10:22.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:10:22.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:10:22.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:10:24.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:10:26.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:10:28.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:10:30.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:10:32.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:10:34.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:10:36.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:10:38.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:10:40.994 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:10:40.994 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 11:10:40.995 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 11:10:40.995 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:10:41.023 | 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

2025-09-01 11:10:41.024 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:10:41.123 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:10:41.213 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch18
2025-09-01 11:10:44.398 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 18/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, 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: 1.996e-03, size: 352, ETA: 3:17:35
2025-09-01 11:10:47.622 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 18/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.996e-03, size: 320, ETA: 3:17:33
2025-09-01 11:10:50.859 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 18/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.161s, 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: 448, ETA: 3:17:32
2025-09-01 11:10:54.065 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 18/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.996e-03, size: 320, ETA: 3:17:29
2025-09-01 11:10:57.252 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 18/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.7, lr: 1.996e-03, size: 288, ETA: 3:17:26
2025-09-01 11:11:00.377 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 18/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.996e-03, size: 352, ETA: 3:17:20
2025-09-01 11:11:01.752 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:11:07.926 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:11:09.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:11:11.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5595
2025-09-01 11:11:11.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4970
2025-09-01 11:11:11.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2698
2025-09-01 11:11:11.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4421
2025-09-01 11:11:11.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:11:11.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:11:11.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.560
2025-09-01 11:11:11.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-09-01 11:11:11.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.270
2025-09-01 11:11:11.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.442
2025-09-01 11:11:11.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:11:11.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:11:11.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:11:11.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:11:11.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:11:11.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:11:11.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:11:11.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:11:11.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:11:13.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:11:14.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:11:16.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:11:18.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:11:19.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:11:21.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:11:23.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:11:24.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:11:26.599 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:11:26.600 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 11:11:26.600 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 11:11:26.600 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:11:26.624 | 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: 7.07 ms

2025-09-01 11:11:26.626 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:11:26.712 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:11:26.798 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch19
2025-09-01 11:11:29.947 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 19/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.995e-03, size: 320, ETA: 3:17:12
2025-09-01 11:11:33.105 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 19/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.995e-03, size: 448, ETA: 3:17:08
2025-09-01 11:11:36.407 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 19/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.995e-03, size: 448, ETA: 3:17:07
2025-09-01 11:11:39.660 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 19/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.161s, 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: 1.995e-03, size: 416, ETA: 3:17:06
2025-09-01 11:11:42.962 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 19/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.995e-03, size: 288, ETA: 3:17:07
2025-09-01 11:11:46.174 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 19/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.158s, 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: 1.995e-03, size: 288, ETA: 3:17:04
2025-09-01 11:11:47.629 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:11:53.943 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:11:57.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:11:59.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5317
2025-09-01 11:11:59.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4445
2025-09-01 11:11:59.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2376
2025-09-01 11:11:59.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4046
2025-09-01 11:11:59.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:11:59.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:11:59.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-09-01 11:11:59.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.444
2025-09-01 11:11:59.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.238
2025-09-01 11:11:59.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.405
2025-09-01 11:11:59.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:11:59.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:11:59.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:11:59.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:11:59.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:11:59.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:11:59.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:11:59.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:11:59.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:12:02.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:12:04.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:12:07.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:12:10.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:12:12.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:12:15.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:12:17.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:12:20.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:12:22.866 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:12:22.867 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 11:12:22.867 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-09-01 11:12:22.867 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:12:22.893 | 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

2025-09-01 11:12:22.893 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:12:23.022 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:12:23.108 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch20
2025-09-01 11:12:26.195 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 20/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.6, lr: 1.995e-03, size: 544, ETA: 3:16:57
2025-09-01 11:12:29.442 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 20/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.2, lr: 1.995e-03, size: 288, ETA: 3:16:55
2025-09-01 11:12:32.771 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 20/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.995e-03, size: 256, ETA: 3:16:56
2025-09-01 11:12:36.084 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 20/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.2, lr: 1.995e-03, size: 448, ETA: 3:16:56
2025-09-01 11:12:39.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 20/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.8, lr: 1.995e-03, size: 512, ETA: 3:16:52
2025-09-01 11:12:42.608 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 20/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.995e-03, size: 512, ETA: 3:16:54
2025-09-01 11:12:44.108 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:12:50.364 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:12:52.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:12:53.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5665
2025-09-01 11:12:53.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5043
2025-09-01 11:12:53.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3028
2025-09-01 11:12:53.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4579
2025-09-01 11:12:53.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:12:53.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:12:53.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.567
2025-09-01 11:12:53.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-09-01 11:12:53.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-09-01 11:12:53.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.458
2025-09-01 11:12:53.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:12:53.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:12:53.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:12:53.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:12:53.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:12:53.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:12:53.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:12:53.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:12:53.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:12:55.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:12:56.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:12:58.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:12:59.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:13:01.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:13:02.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:13:04.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:13:05.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:13:07.210 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:13:07.211 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 11:13:07.211 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 11:13:07.211 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:13:07.241 | 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: 7.26 ms

2025-09-01 11:13:07.242 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:13:07.325 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:13:07.412 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch21
2025-09-01 11:13:10.542 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 21/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.155s, 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: 1.994e-03, size: 544, ETA: 3:16:49
2025-09-01 11:13:13.758 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 21/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.994e-03, size: 288, ETA: 3:16:47
2025-09-01 11:13:17.301 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 21/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.994e-03, size: 576, ETA: 3:16:53
2025-09-01 11:13:20.472 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 21/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.994e-03, size: 256, ETA: 3:16:49
2025-09-01 11:13:23.763 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 21/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.994e-03, size: 256, ETA: 3:16:48
2025-09-01 11:13:27.042 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 21/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.994e-03, size: 256, ETA: 3:16:46
2025-09-01 11:13:28.635 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:13:34.874 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:13:36.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:13:37.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5057
2025-09-01 11:13:37.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4392
2025-09-01 11:13:37.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2337
2025-09-01 11:13:37.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3929
2025-09-01 11:13:37.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:13:37.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:13:37.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-09-01 11:13:37.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-09-01 11:13:37.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.234
2025-09-01 11:13:37.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.393
2025-09-01 11:13:37.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:13:37.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:13:37.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:13:37.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:13:37.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:13:37.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:13:37.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:13:37.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:13:37.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:13:38.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:13:39.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:13:40.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:13:41.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:13:42.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:13:43.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:13:44.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:13:45.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:13:46.901 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:13:46.901 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-09-01 11:13:46.901 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-09-01 11:13:46.902 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:13:46.909 | 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

2025-09-01 11:13:46.911 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:13:46.992 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:13:47.085 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch22
2025-09-01 11:13:50.201 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 22/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.2, lr: 1.994e-03, size: 544, ETA: 3:16:44
2025-09-01 11:13:53.454 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 22/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.994e-03, size: 576, ETA: 3:16:41
2025-09-01 11:13:56.724 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 22/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.162s, 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: 1.994e-03, size: 288, ETA: 3:16:40
2025-09-01 11:13:59.938 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 22/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.7, lr: 1.994e-03, size: 576, ETA: 3:16:37
2025-09-01 11:14:03.224 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 22/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.994e-03, size: 352, ETA: 3:16:36
2025-09-01 11:14:06.607 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 22/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.167s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.993e-03, size: 544, ETA: 3:16:38
2025-09-01 11:14:08.060 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:14:14.346 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:14:16.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:14:17.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5544
2025-09-01 11:14:17.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5057
2025-09-01 11:14:18.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3070
2025-09-01 11:14:18.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4557
2025-09-01 11:14:18.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:14:18.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:14:18.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.554
2025-09-01 11:14:18.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-09-01 11:14:18.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.307
2025-09-01 11:14:18.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.456
2025-09-01 11:14:18.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:14:18.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:14:18.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:14:18.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:14:18.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:14:18.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:14:18.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:14:18.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:14:18.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:14:19.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:14:21.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:14:23.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:14:24.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:14:26.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:14:28.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:14:29.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:14:31.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:14:32.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:14:32.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 11:14:32.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 11:14:32.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:14:33.006 | 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: 7.14 ms

2025-09-01 11:14:33.008 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:14:33.178 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:14:33.263 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch23
2025-09-01 11:14:36.507 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 23/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.993e-03, size: 512, ETA: 3:16:34
2025-09-01 11:14:39.689 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 23/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.993e-03, size: 480, ETA: 3:16:30
2025-09-01 11:14:42.914 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 23/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.993e-03, size: 288, ETA: 3:16:27
2025-09-01 11:14:46.170 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 23/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.160s, 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: 1.993e-03, size: 320, ETA: 3:16:25
2025-09-01 11:14:49.394 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 23/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.993e-03, size: 288, ETA: 3:16:22
2025-09-01 11:14:52.788 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 23/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, 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: 1.993e-03, size: 448, ETA: 3:16:23
2025-09-01 11:14:54.254 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:15:00.498 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:15:03.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:15:05.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5464
2025-09-01 11:15:05.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4946
2025-09-01 11:15:05.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2900
2025-09-01 11:15:05.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4437
2025-09-01 11:15:05.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:15:05.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:15:05.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.546
2025-09-01 11:15:05.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-09-01 11:15:05.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-09-01 11:15:05.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.444
2025-09-01 11:15:05.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:15:05.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:15:05.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:15:05.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:15:05.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:15:05.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:15:05.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:15:05.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:15:05.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:15:08.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:15:10.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:15:13.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:15:15.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:15:18.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:15:20.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:15:23.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:15:25.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:15:27.936 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:15:27.936 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:15:27.936 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 11:15:27.936 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:15:27.961 | 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

2025-09-01 11:15:27.962 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:15:28.054 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:15:28.141 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch24
2025-09-01 11:15:31.214 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 24/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.993e-03, size: 320, ETA: 3:16:16
2025-09-01 11:15:34.450 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 24/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.159s, 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: 1.993e-03, size: 384, ETA: 3:16:13
2025-09-01 11:15:37.671 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 24/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, 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: 1.992e-03, size: 320, ETA: 3:16:10
2025-09-01 11:15:40.924 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 24/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.161s, 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: 1.992e-03, size: 256, ETA: 3:16:08
2025-09-01 11:15:44.264 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 24/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.165s, 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: 1.992e-03, size: 544, ETA: 3:16:09
2025-09-01 11:15:47.645 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 24/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.992e-03, size: 512, ETA: 3:16:09
2025-09-01 11:15:49.160 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:15:55.432 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:15:58.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:16:00.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5307
2025-09-01 11:16:00.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4971
2025-09-01 11:16:00.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2707
2025-09-01 11:16:00.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4328
2025-09-01 11:16:00.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:16:00.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:16:00.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-09-01 11:16:00.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-09-01 11:16:00.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.271
2025-09-01 11:16:00.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.433
2025-09-01 11:16:00.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:16:00.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:16:00.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:16:00.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:16:00.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:16:00.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:16:00.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:16:00.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:16:00.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:16:03.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:16:05.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:16:07.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:16:10.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:16:12.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:16:15.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:16:17.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:16:19.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:16:22.051 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:16:22.051 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 11:16:22.051 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 11:16:22.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:16:22.076 | 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: 7.08 ms

2025-09-01 11:16:22.078 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:16:22.163 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:16:22.252 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch25
2025-09-01 11:16:25.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 25/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.992e-03, size: 416, ETA: 3:16:06
2025-09-01 11:16:28.716 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 25/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.992e-03, size: 288, ETA: 3:16:05
2025-09-01 11:16:32.081 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 25/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.992e-03, size: 352, ETA: 3:16:05
2025-09-01 11:16:35.451 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 25/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.992e-03, size: 288, ETA: 3:16:06
2025-09-01 11:16:38.651 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 25/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.2, lr: 1.992e-03, size: 512, ETA: 3:16:02
2025-09-01 11:16:42.000 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 25/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.991e-03, size: 480, ETA: 3:16:02
2025-09-01 11:16:43.489 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:16:49.652 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:16:51.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:16:53.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5440
2025-09-01 11:16:54.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4532
2025-09-01 11:16:54.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2573
2025-09-01 11:16:54.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4182
2025-09-01 11:16:54.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:16:54.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:16:54.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.544
2025-09-01 11:16:54.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.453
2025-09-01 11:16:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.257
2025-09-01 11:16:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.418
2025-09-01 11:16:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:16:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:16:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:16:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:16:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:16:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:16:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:16:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:16:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:16:56.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:16:58.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:17:00.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:17:02.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:17:04.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:17:06.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:17:08.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:17:10.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:17:12.838 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:17:12.838 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-09-01 11:17:12.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-09-01 11:17:12.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:17:12.867 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.96 ms, Average inference time: 7.05 ms

2025-09-01 11:17:12.868 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:17:12.968 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:17:13.059 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch26
2025-09-01 11:17:16.229 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 26/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.991e-03, size: 576, ETA: 3:15:57
2025-09-01 11:17:19.540 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 26/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.163s, 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: 1.991e-03, size: 416, ETA: 3:15:56
2025-09-01 11:17:22.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 26/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.167s, 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: 1.991e-03, size: 256, ETA: 3:15:57
2025-09-01 11:17:26.274 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 26/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.164s, 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: 1.991e-03, size: 320, ETA: 3:15:56
2025-09-01 11:17:29.443 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 26/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.991e-03, size: 320, ETA: 3:15:52
2025-09-01 11:17:32.588 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 26/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.155s, data_time: 0.005s, total_loss: 7.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.991e-03, size: 256, ETA: 3:15:47
2025-09-01 11:17:34.068 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:17:40.362 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:17:43.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:17:45.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5613
2025-09-01 11:17:45.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4958
2025-09-01 11:17:45.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2755
2025-09-01 11:17:45.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4442
2025-09-01 11:17:45.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:17:45.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:17:45.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.561
2025-09-01 11:17:45.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-09-01 11:17:45.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.276
2025-09-01 11:17:45.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.444
2025-09-01 11:17:45.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:17:45.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:17:45.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:17:45.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:17:45.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:17:45.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:17:45.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:17:45.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:17:45.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:17:48.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:17:50.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:17:52.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:17:55.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:17:57.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:18:00.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:18:02.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:18:04.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:18:07.093 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:18:07.094 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 11:18:07.094 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 11:18:07.094 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:18:07.122 | 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

2025-09-01 11:18:07.129 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:18:07.222 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:18:07.314 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch27
2025-09-01 11:18:10.413 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 27/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.991e-03, size: 288, ETA: 3:15:41
2025-09-01 11:18:13.791 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 27/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.167s, 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: 1.991e-03, size: 416, ETA: 3:15:41
2025-09-01 11:18:17.111 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 27/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.165s, 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: 1.990e-03, size: 320, ETA: 3:15:41
2025-09-01 11:18:20.356 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 27/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.5Gb, 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: 1.990e-03, size: 384, ETA: 3:15:39
2025-09-01 11:18:23.499 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 27/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.990e-03, size: 288, ETA: 3:15:34
2025-09-01 11:18:26.656 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 27/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.5Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.8, lr: 1.990e-03, size: 512, ETA: 3:15:29
2025-09-01 11:18:28.162 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:18:34.527 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:18:36.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:18:38.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5377
2025-09-01 11:18:38.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4562
2025-09-01 11:18:38.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3231
2025-09-01 11:18:38.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4390
2025-09-01 11:18:38.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:18:38.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:18:38.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.538
2025-09-01 11:18:38.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.456
2025-09-01 11:18:38.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.323
2025-09-01 11:18:38.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.439
2025-09-01 11:18:38.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:18:38.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:18:38.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:18:38.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:18:38.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:18:38.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:18:38.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:18:38.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:18:38.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:18:40.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:18:42.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:18:44.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:18:46.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:18:48.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:18:50.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:18:52.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:18:54.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:18:56.202 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:18:56.203 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:18:56.203 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 11:18:56.203 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:18:56.228 | 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

2025-09-01 11:18:56.229 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:18:56.319 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:18:56.406 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch28
2025-09-01 11:18:59.590 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 28/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.990e-03, size: 384, ETA: 3:15:26
2025-09-01 11:19:02.935 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 28/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.164s, 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: 1.990e-03, size: 288, ETA: 3:15:25
2025-09-01 11:19:06.460 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 28/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.173s, 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: 1.990e-03, size: 480, ETA: 3:15:28
2025-09-01 11:19:10.005 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 28/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.175s, data_time: 0.004s, total_loss: 8.1, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.8, lr: 1.990e-03, size: 448, ETA: 3:15:31
2025-09-01 11:19:13.537 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 28/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.173s, 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: 1.989e-03, size: 288, ETA: 3:15:34
2025-09-01 11:19:16.998 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 28/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.170s, data_time: 0.003s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.989e-03, size: 416, ETA: 3:15:36
2025-09-01 11:19:18.639 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:19:24.958 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:19:26.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:19:28.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5421
2025-09-01 11:19:28.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4941
2025-09-01 11:19:28.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3006
2025-09-01 11:19:28.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4456
2025-09-01 11:19:28.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:19:28.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:19:28.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-09-01 11:19:28.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-09-01 11:19:28.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.301
2025-09-01 11:19:28.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.446
2025-09-01 11:19:28.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:19:28.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:19:28.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:19:28.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:19:28.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:19:28.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:19:28.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:19:28.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:19:28.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:19:30.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:19:32.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:19:33.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:19:35.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:19:36.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:19:38.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:19:40.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:19:41.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:19:43.622 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:19:43.622 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:19:43.623 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 11:19:43.623 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:19:43.647 | 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

2025-09-01 11:19:43.650 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:19:43.735 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:19:43.822 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch29
2025-09-01 11:19:47.044 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 29/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.2, lr: 1.989e-03, size: 544, ETA: 3:15:35
2025-09-01 11:19:50.267 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 29/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.159s, 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: 1.989e-03, size: 576, ETA: 3:15:32
2025-09-01 11:19:53.575 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 29/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.989e-03, size: 288, ETA: 3:15:30
2025-09-01 11:19:56.827 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 29/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.989e-03, size: 288, ETA: 3:15:28
2025-09-01 11:20:00.085 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 29/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.160s, 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: 1.989e-03, size: 320, ETA: 3:15:25
2025-09-01 11:20:03.433 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 29/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.1, lr: 1.989e-03, size: 416, ETA: 3:15:24
2025-09-01 11:20:04.879 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:20:11.076 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:20:13.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:20:16.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5616
2025-09-01 11:20:16.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4818
2025-09-01 11:20:16.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3247
2025-09-01 11:20:16.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4561
2025-09-01 11:20:16.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:20:16.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:20:16.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.562
2025-09-01 11:20:16.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-09-01 11:20:16.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.325
2025-09-01 11:20:16.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.456
2025-09-01 11:20:16.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:20:16.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:20:16.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:20:16.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:20:16.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:20:16.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:20:16.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:20:16.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:20:16.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:20:18.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:20:21.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:20:23.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:20:26.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:20:28.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:20:30.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:20:33.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:20:35.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:20:37.870 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:20:37.870 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 11:20:37.870 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 11:20:37.870 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:20:37.896 | 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: 7.19 ms

2025-09-01 11:20:37.897 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:20:37.976 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:20:38.065 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch30
2025-09-01 11:20:41.294 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 30/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.988e-03, size: 320, ETA: 3:15:20
2025-09-01 11:20:44.478 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 30/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.988e-03, size: 416, ETA: 3:15:16
2025-09-01 11:20:47.868 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 30/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.6Gb, 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: 1.988e-03, size: 480, ETA: 3:15:16
2025-09-01 11:20:51.236 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 30/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.988e-03, size: 448, ETA: 3:15:15
2025-09-01 11:20:54.563 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 30/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.164s, 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: 1.988e-03, size: 352, ETA: 3:15:14
2025-09-01 11:20:57.836 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 30/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.162s, 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: 1.988e-03, size: 320, ETA: 3:15:12
2025-09-01 11:20:59.234 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:21:05.462 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:21:07.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:21:09.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5489
2025-09-01 11:21:09.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4980
2025-09-01 11:21:09.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3139
2025-09-01 11:21:09.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4536
2025-09-01 11:21:09.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:21:09.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:21:09.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.549
2025-09-01 11:21:09.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-09-01 11:21:09.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-09-01 11:21:09.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.454
2025-09-01 11:21:09.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:21:09.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:21:09.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:21:09.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:21:09.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:21:09.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:21:09.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:21:09.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:21:09.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:21:11.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:21:13.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:21:14.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:21:16.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:21:18.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:21:19.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:21:21.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:21:23.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:21:24.752 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:21:24.752 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 11:21:24.752 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 11:21:24.752 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:21:24.780 | 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

2025-09-01 11:21:24.781 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:21:24.905 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:21:24.997 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch31
2025-09-01 11:21:28.098 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 31/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.153s, 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: 1.988e-03, size: 352, ETA: 3:15:04
2025-09-01 11:21:31.338 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 31/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.160s, 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: 1.987e-03, size: 384, ETA: 3:15:01
2025-09-01 11:21:34.659 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 31/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.987e-03, size: 480, ETA: 3:14:59
2025-09-01 11:21:37.748 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 31/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.153s, 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: 1.987e-03, size: 288, ETA: 3:14:54
2025-09-01 11:21:40.872 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 31/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.987e-03, size: 288, ETA: 3:14:49
2025-09-01 11:21:44.133 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 31/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.161s, data_time: 0.006s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.5, lr: 1.987e-03, size: 256, ETA: 3:14:47
2025-09-01 11:21:45.533 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:21:51.725 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:21:54.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:21:55.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5033
2025-09-01 11:21:55.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4349
2025-09-01 11:21:55.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2894
2025-09-01 11:21:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4092
2025-09-01 11:21:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:21:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:21:55.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-09-01 11:21:55.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.435
2025-09-01 11:21:55.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.289
2025-09-01 11:21:55.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.409
2025-09-01 11:21:55.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:21:55.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:21:55.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:21:55.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:21:55.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:21:55.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:21:55.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:21:55.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:21:55.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:21:57.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:21:59.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:22:01.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:22:03.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:22:05.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:22:07.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:22:09.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:22:11.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:22:13.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:22:13.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-09-01 11:22:13.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-09-01 11:22:13.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:22:13.222 | 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: 7.15 ms

2025-09-01 11:22:13.224 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:22:13.313 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:22:13.399 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch32
2025-09-01 11:22:16.547 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 32/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.156s, 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: 1.987e-03, size: 448, ETA: 3:14:40
2025-09-01 11:22:19.824 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 32/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.987e-03, size: 544, ETA: 3:14:38
2025-09-01 11:22:23.041 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 32/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.986e-03, size: 448, ETA: 3:14:35
2025-09-01 11:22:26.284 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 32/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.986e-03, size: 416, ETA: 3:14:32
2025-09-01 11:22:29.570 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 32/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.163s, 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: 1.986e-03, size: 544, ETA: 3:14:30
2025-09-01 11:22:32.949 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 32/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.167s, data_time: 0.003s, total_loss: 8.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.4, cls_loss: 1.3, lr: 1.986e-03, size: 544, ETA: 3:14:30
2025-09-01 11:22:34.485 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:22:40.828 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:22:43.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:22:44.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5680
2025-09-01 11:22:44.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4842
2025-09-01 11:22:44.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2991
2025-09-01 11:22:44.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4504
2025-09-01 11:22:44.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:22:44.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:22:44.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.568
2025-09-01 11:22:44.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-09-01 11:22:44.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.299
2025-09-01 11:22:44.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.450
2025-09-01 11:22:44.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:22:44.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:22:44.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:22:44.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:22:44.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:22:44.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:22:44.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:22:44.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:22:44.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:22:46.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:22:48.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:22:50.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:22:52.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:22:54.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:22:56.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:22:58.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:23:00.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:23:02.025 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:23:02.025 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 11:23:02.025 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 11:23:02.025 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:23:02.052 | 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

2025-09-01 11:23:02.056 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:23:02.147 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:23:02.236 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch33
2025-09-01 11:23:05.510 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 33/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, 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: 0.8, lr: 1.986e-03, size: 512, ETA: 3:14:27
2025-09-01 11:23:08.795 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 33/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.162s, 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: 1.986e-03, size: 320, ETA: 3:14:25
2025-09-01 11:23:11.983 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 33/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.2, lr: 1.986e-03, size: 352, ETA: 3:14:21
2025-09-01 11:23:15.170 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 33/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.985e-03, size: 320, ETA: 3:14:17
2025-09-01 11:23:18.465 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 33/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.985e-03, size: 544, ETA: 3:14:14
2025-09-01 11:23:21.794 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 33/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.164s, 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: 1.985e-03, size: 576, ETA: 3:14:13
2025-09-01 11:23:23.366 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:23:29.624 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:23:34.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:23:38.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5410
2025-09-01 11:23:38.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4702
2025-09-01 11:23:38.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2866
2025-09-01 11:23:38.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4326
2025-09-01 11:23:38.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:23:38.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:23:38.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-09-01 11:23:38.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.470
2025-09-01 11:23:38.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.287
2025-09-01 11:23:38.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.433
2025-09-01 11:23:38.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:23:38.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:23:38.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:23:38.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:23:38.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:23:38.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:23:38.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:23:38.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:23:38.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:23:43.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:23:47.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:23:51.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:23:55.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:23:59.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:24:03.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:24:08.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:24:12.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:24:16.466 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:24:16.467 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:24:16.467 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 11:24:16.467 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:24:16.493 | 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

2025-09-01 11:24:16.494 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:24:16.575 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:24:16.660 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch34
2025-09-01 11:24:19.799 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 34/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.154s, 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: 1.985e-03, size: 512, ETA: 3:14:09
2025-09-01 11:24:23.085 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 34/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 1.0, lr: 1.985e-03, size: 256, ETA: 3:14:06
2025-09-01 11:24:26.208 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 34/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 1.0, lr: 1.985e-03, size: 320, ETA: 3:14:01
2025-09-01 11:24:29.312 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 34/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.153s, 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: 1.985e-03, size: 320, ETA: 3:13:56
2025-09-01 11:24:32.487 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 34/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.158s, 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: 1.984e-03, size: 384, ETA: 3:13:52
2025-09-01 11:24:35.849 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 34/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.984e-03, size: 544, ETA: 3:13:51
2025-09-01 11:24:37.270 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:24:43.537 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:24:46.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:24:47.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5445
2025-09-01 11:24:48.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4971
2025-09-01 11:24:48.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2934
2025-09-01 11:24:48.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4450
2025-09-01 11:24:48.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:24:48.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:24:48.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-09-01 11:24:48.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-09-01 11:24:48.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.293
2025-09-01 11:24:48.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.445
2025-09-01 11:24:48.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:24:48.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:24:48.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:24:48.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:24:48.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:24:48.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:24:48.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:24:48.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:24:48.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:24:50.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:24:52.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:24:54.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:24:56.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:24:58.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:25:00.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:25:02.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:25:04.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:25:06.746 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:25:06.747 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:25:06.747 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 11:25:06.747 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:25:06.772 | 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

2025-09-01 11:25:06.779 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:25:06.863 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:25:06.953 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch35
2025-09-01 11:25:10.110 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 35/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.984e-03, size: 512, ETA: 3:13:45
2025-09-01 11:25:13.400 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 35/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.163s, 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: 1.984e-03, size: 416, ETA: 3:13:43
2025-09-01 11:25:16.703 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 35/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.984e-03, size: 384, ETA: 3:13:41
2025-09-01 11:25:19.952 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 35/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.984e-03, size: 416, ETA: 3:13:38
2025-09-01 11:25:23.260 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 35/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.162s, 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: 1.983e-03, size: 256, ETA: 3:13:36
2025-09-01 11:25:26.513 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 35/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.983e-03, size: 384, ETA: 3:13:33
2025-09-01 11:25:27.938 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:25:34.299 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:25:36.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:25:38.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5388
2025-09-01 11:25:38.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4972
2025-09-01 11:25:38.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2697
2025-09-01 11:25:38.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4353
2025-09-01 11:25:38.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:25:38.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:25:38.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-09-01 11:25:38.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-09-01 11:25:38.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.270
2025-09-01 11:25:38.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.435
2025-09-01 11:25:38.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:25:38.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:25:38.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:25:38.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:25:38.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:25:38.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:25:38.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:25:38.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:25:38.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:25:40.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:25:42.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:25:44.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:25:46.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:25:48.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:25:50.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:25:52.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:25:54.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:25:56.462 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:25:56.462 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 11:25:56.463 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 11:25:56.463 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:25:56.493 | 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

2025-09-01 11:25:56.494 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:25:56.610 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:25:56.763 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch36
2025-09-01 11:25:59.982 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 36/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.2, lr: 1.983e-03, size: 576, ETA: 3:13:28
2025-09-01 11:26:03.356 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 36/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.983e-03, size: 512, ETA: 3:13:27
2025-09-01 11:26:06.779 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 36/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.169s, 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: 1.983e-03, size: 320, ETA: 3:13:27
2025-09-01 11:26:10.060 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 36/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.161s, 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: 1.983e-03, size: 384, ETA: 3:13:24
2025-09-01 11:26:13.359 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 36/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.164s, 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: 1.983e-03, size: 352, ETA: 3:13:22
2025-09-01 11:26:16.613 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 36/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.982e-03, size: 448, ETA: 3:13:20
2025-09-01 11:26:18.123 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:26:24.458 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:26:27.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:26:29.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5590
2025-09-01 11:26:29.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5074
2025-09-01 11:26:29.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3224
2025-09-01 11:26:29.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4630
2025-09-01 11:26:29.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:26:29.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:26:29.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-09-01 11:26:29.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-09-01 11:26:29.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.322
2025-09-01 11:26:29.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.463
2025-09-01 11:26:29.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:26:29.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:26:29.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:26:29.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:26:29.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:26:29.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:26:29.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:26:29.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:26:29.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:26:31.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:26:34.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:26:36.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:26:38.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:26:41.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:26:43.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:26:45.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:26:47.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:26:50.157 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:26:50.157 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 11:26:50.157 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 11:26:50.158 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:26:50.185 | 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

2025-09-01 11:26:50.186 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:26:50.278 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:26:50.380 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch37
2025-09-01 11:26:53.542 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 37/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.982e-03, size: 384, ETA: 3:13:15
2025-09-01 11:26:56.635 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 37/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.152s, 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: 1.982e-03, size: 416, ETA: 3:13:10
2025-09-01 11:26:59.829 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 37/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.982e-03, size: 512, ETA: 3:13:06
2025-09-01 11:27:03.077 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 37/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.161s, 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: 1.982e-03, size: 544, ETA: 3:13:03
2025-09-01 11:27:06.346 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 37/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.982e-03, size: 480, ETA: 3:13:00
2025-09-01 11:27:09.562 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 37/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.981e-03, size: 416, ETA: 3:12:57
2025-09-01 11:27:11.034 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:27:17.390 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:27:19.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:27:21.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4941
2025-09-01 11:27:21.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4902
2025-09-01 11:27:21.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2876
2025-09-01 11:27:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4240
2025-09-01 11:27:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:27:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:27:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-09-01 11:27:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-09-01 11:27:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.288
2025-09-01 11:27:21.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.424
2025-09-01 11:27:21.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:27:21.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:27:21.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:27:21.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:27:21.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:27:21.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:27:21.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:27:21.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:27:21.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:27:23.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:27:25.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:27:27.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:27:29.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:27:31.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:27:33.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:27:34.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:27:36.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:27:38.644 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:27:38.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-09-01 11:27:38.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-09-01 11:27:38.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:27:38.672 | 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

2025-09-01 11:27:38.673 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:27:38.755 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:27:38.841 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch38
2025-09-01 11:27:41.941 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 38/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.153s, 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: 1.981e-03, size: 352, ETA: 3:12:51
2025-09-01 11:27:45.045 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 38/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.981e-03, size: 352, ETA: 3:12:45
2025-09-01 11:27:48.195 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 38/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.981e-03, size: 288, ETA: 3:12:41
2025-09-01 11:27:51.457 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 38/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.161s, 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: 1.981e-03, size: 256, ETA: 3:12:39
2025-09-01 11:27:54.669 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 38/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.981e-03, size: 480, ETA: 3:12:35
2025-09-01 11:27:57.984 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 38/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.164s, data_time: 0.006s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.980e-03, size: 544, ETA: 3:12:33
2025-09-01 11:27:59.499 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:28:05.752 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:28:08.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:28:09.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5525
2025-09-01 11:28:09.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4879
2025-09-01 11:28:10.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2465
2025-09-01 11:28:10.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4290
2025-09-01 11:28:10.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:28:10.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:28:10.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.553
2025-09-01 11:28:10.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-09-01 11:28:10.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.247
2025-09-01 11:28:10.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.429
2025-09-01 11:28:10.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:28:10.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:28:10.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:28:10.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:28:10.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:28:10.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:28:10.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:28:10.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:28:10.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:28:12.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:28:14.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:28:15.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:28:17.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:28:19.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:28:21.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:28:23.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:28:25.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:28:27.426 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:28:27.427 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:28:27.427 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 11:28:27.427 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:28:27.452 | 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

2025-09-01 11:28:27.453 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:28:27.575 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:28:27.663 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch39
2025-09-01 11:28:30.958 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 39/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.6Gb, 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: 1.980e-03, size: 288, ETA: 3:12:30
2025-09-01 11:28:34.153 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 39/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.980e-03, size: 576, ETA: 3:12:26
2025-09-01 11:28:37.658 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 39/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.980e-03, size: 384, ETA: 3:12:27
2025-09-01 11:28:40.807 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 39/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.980e-03, size: 448, ETA: 3:12:23
2025-09-01 11:28:44.128 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 39/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.979e-03, size: 512, ETA: 3:12:21
2025-09-01 11:28:47.416 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 39/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.6Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 6.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.979e-03, size: 352, ETA: 3:12:19
2025-09-01 11:28:48.903 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:28:55.164 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:28:59.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:29:01.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4851
2025-09-01 11:29:02.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4688
2025-09-01 11:29:02.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2292
2025-09-01 11:29:02.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3944
2025-09-01 11:29:02.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:29:02.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:29:02.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-09-01 11:29:02.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-09-01 11:29:02.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.229
2025-09-01 11:29:02.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.394
2025-09-01 11:29:02.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:29:02.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:29:02.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:29:02.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:29:02.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:29:02.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:29:02.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:29:02.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:29:02.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:29:05.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:29:09.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:29:12.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:29:15.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:29:19.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:29:22.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:29:25.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:29:28.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:29:32.246 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:29:32.247 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-09-01 11:29:32.247 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-09-01 11:29:32.247 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:29:32.273 | 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: 7.22 ms

2025-09-01 11:29:32.273 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:29:32.352 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:29:32.441 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch40
2025-09-01 11:29:35.595 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 40/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.7, lr: 1.979e-03, size: 512, ETA: 3:12:13
2025-09-01 11:29:38.860 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 40/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.161s, 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: 1.979e-03, size: 320, ETA: 3:12:11
2025-09-01 11:29:42.019 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 40/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.979e-03, size: 576, ETA: 3:12:07
2025-09-01 11:29:45.406 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 40/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.167s, 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: 1.979e-03, size: 288, ETA: 3:12:06
2025-09-01 11:29:48.523 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 40/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.978e-03, size: 576, ETA: 3:12:01
2025-09-01 11:29:51.832 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 40/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.978e-03, size: 256, ETA: 3:11:59
2025-09-01 11:29:53.200 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:29:59.516 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:30:04.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:30:07.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5482
2025-09-01 11:30:07.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4655
2025-09-01 11:30:08.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2767
2025-09-01 11:30:08.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4301
2025-09-01 11:30:08.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:30:08.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:30:08.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.548
2025-09-01 11:30:08.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-09-01 11:30:08.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.277
2025-09-01 11:30:08.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.430
2025-09-01 11:30:08.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:30:08.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:30:08.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:30:08.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:30:08.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:30:08.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:30:08.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:30:08.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:30:08.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:30:12.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:30:16.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:30:19.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:30:23.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:30:27.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:30:31.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:30:35.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:30:39.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:30:43.238 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:30:43.239 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 11:30:43.239 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 11:30:43.239 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:30:43.265 | 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: 7.21 ms

2025-09-01 11:30:43.266 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:30:43.399 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:30:43.493 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch41
2025-09-01 11:30:46.627 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 41/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.978e-03, size: 256, ETA: 3:11:52
2025-09-01 11:30:49.912 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 41/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, 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: 1.978e-03, size: 288, ETA: 3:11:49
2025-09-01 11:30:53.178 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 41/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.4, cls_loss: 1.1, lr: 1.978e-03, size: 544, ETA: 3:11:46
2025-09-01 11:30:56.490 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 41/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.163s, 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: 1.977e-03, size: 384, ETA: 3:11:44
2025-09-01 11:30:59.884 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 41/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.167s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.977e-03, size: 320, ETA: 3:11:43
2025-09-01 11:31:03.252 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 41/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.977e-03, size: 544, ETA: 3:11:41
2025-09-01 11:31:04.706 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:31:10.987 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:31:15.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:31:18.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5304
2025-09-01 11:31:19.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4696
2025-09-01 11:31:19.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2043
2025-09-01 11:31:19.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4014
2025-09-01 11:31:19.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:31:19.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:31:19.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-09-01 11:31:19.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.470
2025-09-01 11:31:19.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.204
2025-09-01 11:31:19.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.401
2025-09-01 11:31:19.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:31:19.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:31:19.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:31:19.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:31:19.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:31:19.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:31:19.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:31:19.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:31:19.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:31:22.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:31:26.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:31:30.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:31:33.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:31:37.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:31:41.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:31:44.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:31:48.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:31:51.983 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:31:51.984 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 11:31:51.984 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-09-01 11:31:51.984 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:31:52.009 | 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

2025-09-01 11:31:52.012 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:31:52.154 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:31:52.246 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch42
2025-09-01 11:31:55.301 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 42/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.152s, 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: 1.977e-03, size: 320, ETA: 3:11:35
2025-09-01 11:31:58.428 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 42/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.977e-03, size: 352, ETA: 3:11:30
2025-09-01 11:32:01.696 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 42/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.977e-03, size: 576, ETA: 3:11:27
2025-09-01 11:32:04.910 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 42/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.159s, 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: 1.976e-03, size: 352, ETA: 3:11:24
2025-09-01 11:32:08.078 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 42/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.976e-03, size: 512, ETA: 3:11:20
2025-09-01 11:32:11.343 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 42/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, 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: 0.9, lr: 1.976e-03, size: 512, ETA: 3:11:17
2025-09-01 11:32:12.789 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:32:19.167 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:32:21.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:32:23.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5658
2025-09-01 11:32:23.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4850
2025-09-01 11:32:23.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3035
2025-09-01 11:32:23.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4515
2025-09-01 11:32:23.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:32:23.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:32:23.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.566
2025-09-01 11:32:23.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-09-01 11:32:23.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-09-01 11:32:23.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.451
2025-09-01 11:32:23.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:32:23.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:32:23.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:32:23.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:32:23.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:32:23.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:32:23.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:32:23.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:32:23.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:32:25.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:32:27.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:32:29.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:32:31.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:32:33.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:32:35.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:32:37.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:32:39.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:32:41.677 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:32:41.677 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 11:32:41.678 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 11:32:41.678 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:32:41.703 | 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

2025-09-01 11:32:41.705 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:32:41.782 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:32:41.870 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch43
2025-09-01 11:32:45.112 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 43/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.7, lr: 1.976e-03, size: 576, ETA: 3:11:13
2025-09-01 11:32:48.502 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 43/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.167s, 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: 1.976e-03, size: 576, ETA: 3:11:12
2025-09-01 11:32:51.846 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 43/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.166s, 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: 1.975e-03, size: 512, ETA: 3:11:10
2025-09-01 11:32:55.079 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 43/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.160s, 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: 1.975e-03, size: 544, ETA: 3:11:07
2025-09-01 11:32:58.539 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 43/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.170s, data_time: 0.003s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.975e-03, size: 384, ETA: 3:11:07
2025-09-01 11:33:01.791 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 43/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.160s, 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: 1.975e-03, size: 512, ETA: 3:11:04
2025-09-01 11:33:03.252 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:33:09.665 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:33:12.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:33:14.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5326
2025-09-01 11:33:14.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4625
2025-09-01 11:33:14.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2692
2025-09-01 11:33:14.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4214
2025-09-01 11:33:14.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:33:14.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:33:14.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-09-01 11:33:14.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-09-01 11:33:14.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.269
2025-09-01 11:33:14.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.421
2025-09-01 11:33:14.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:33:14.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:33:14.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:33:14.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:33:14.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:33:14.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:33:14.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:33:14.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:33:14.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:33:17.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:33:19.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:33:22.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:33:24.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:33:26.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:33:29.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:33:31.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:33:33.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:33:36.120 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:33:36.121 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 11:33:36.121 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-09-01 11:33:36.121 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:33:36.149 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.96 ms, Average inference time: 7.22 ms

2025-09-01 11:33:36.150 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:33:36.238 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:33:36.328 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch44
2025-09-01 11:33:39.456 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 44/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.155s, 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: 1.975e-03, size: 512, ETA: 3:10:58
2025-09-01 11:33:42.782 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 44/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.164s, 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: 1.974e-03, size: 576, ETA: 3:10:56
2025-09-01 11:33:46.107 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 44/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.5, lr: 1.974e-03, size: 384, ETA: 3:10:54
2025-09-01 11:33:49.441 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 44/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.974e-03, size: 352, ETA: 3:10:52
2025-09-01 11:33:52.748 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 44/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.974e-03, size: 384, ETA: 3:10:49
2025-09-01 11:33:56.004 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 44/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, 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: 1.974e-03, size: 416, ETA: 3:10:47
2025-09-01 11:33:57.428 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:34:03.970 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:34:06.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:34:08.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5601
2025-09-01 11:34:08.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4687
2025-09-01 11:34:08.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2802
2025-09-01 11:34:08.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4364
2025-09-01 11:34:08.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:34:08.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:34:08.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.560
2025-09-01 11:34:08.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-09-01 11:34:08.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.280
2025-09-01 11:34:08.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.436
2025-09-01 11:34:08.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:34:08.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:34:08.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:34:08.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:34:08.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:34:08.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:34:08.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:34:08.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:34:08.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:34:10.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:34:12.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:34:14.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:34:16.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:34:18.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:34:20.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:34:22.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:34:23.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:34:25.903 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:34:25.903 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:34:25.903 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 11:34:25.903 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:34:25.935 | 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

2025-09-01 11:34:25.936 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:34:26.012 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:34:26.100 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch45
2025-09-01 11:34:29.348 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 45/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.161s, 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: 1.973e-03, size: 512, ETA: 3:10:42
2025-09-01 11:34:32.573 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 45/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.973e-03, size: 544, ETA: 3:10:39
2025-09-01 11:34:35.867 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 45/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.2, lr: 1.973e-03, size: 384, ETA: 3:10:36
2025-09-01 11:34:39.106 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 45/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.160s, 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: 1.973e-03, size: 512, ETA: 3:10:33
2025-09-01 11:34:42.510 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 45/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.167s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.973e-03, size: 512, ETA: 3:10:32
2025-09-01 11:34:45.924 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 45/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.169s, 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: 1.972e-03, size: 352, ETA: 3:10:31
2025-09-01 11:34:47.325 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:34:53.839 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:34:58.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:35:01.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4981
2025-09-01 11:35:01.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4747
2025-09-01 11:35:01.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2946
2025-09-01 11:35:01.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4225
2025-09-01 11:35:01.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:35:01.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:35:01.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-09-01 11:35:01.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-09-01 11:35:01.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-09-01 11:35:01.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.422
2025-09-01 11:35:01.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:35:01.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:35:01.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:35:01.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:35:01.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:35:01.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:35:01.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:35:01.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:35:01.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:35:05.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:35:08.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:35:12.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:35:16.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:35:19.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:35:23.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:35:26.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:35:30.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:35:33.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:35:33.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:35:33.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-09-01 11:35:33.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:35:34.006 | 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: 7.23 ms

2025-09-01 11:35:34.007 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:35:34.089 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:35:34.175 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch46
2025-09-01 11:35:37.181 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 46/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.147s, 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: 1.972e-03, size: 448, ETA: 3:10:22
2025-09-01 11:35:40.429 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 46/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.8, lr: 1.972e-03, size: 512, ETA: 3:10:19
2025-09-01 11:35:43.800 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 46/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.972e-03, size: 576, ETA: 3:10:18
2025-09-01 11:35:47.035 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 46/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.160s, 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: 1.972e-03, size: 256, ETA: 3:10:15
2025-09-01 11:35:50.235 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 46/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.971e-03, size: 448, ETA: 3:10:11
2025-09-01 11:35:53.653 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 46/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.971e-03, size: 288, ETA: 3:10:10
2025-09-01 11:35:55.124 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:36:01.389 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:36:03.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:36:04.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5553
2025-09-01 11:36:04.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4985
2025-09-01 11:36:04.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2975
2025-09-01 11:36:04.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4504
2025-09-01 11:36:04.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:36:04.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:36:04.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.555
2025-09-01 11:36:04.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-09-01 11:36:04.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.298
2025-09-01 11:36:04.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.450
2025-09-01 11:36:04.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:36:04.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:36:04.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:36:04.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:36:04.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:36:04.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:36:04.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:36:04.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:36:04.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:36:06.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:36:07.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:36:08.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:36:10.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:36:11.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:36:13.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:36:14.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:36:16.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:36:17.683 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:36:17.683 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 11:36:17.683 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 11:36:17.683 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:36:17.693 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.91 ms, Average inference time: 7.20 ms

2025-09-01 11:36:17.696 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:36:17.774 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:36:17.864 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch47
2025-09-01 11:36:21.022 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 47/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.971e-03, size: 576, ETA: 3:10:04
2025-09-01 11:36:24.422 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 47/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.971e-03, size: 320, ETA: 3:10:03
2025-09-01 11:36:27.541 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 47/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.154s, 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: 1.971e-03, size: 288, ETA: 3:09:59
2025-09-01 11:36:30.749 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 47/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.970e-03, size: 352, ETA: 3:09:55
2025-09-01 11:36:34.028 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 47/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.970e-03, size: 416, ETA: 3:09:52
2025-09-01 11:36:37.377 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 47/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.970e-03, size: 416, ETA: 3:09:50
2025-09-01 11:36:38.798 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:36:45.023 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:36:50.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:36:54.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5273
2025-09-01 11:36:55.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4724
2025-09-01 11:36:55.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2655
2025-09-01 11:36:55.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4217
2025-09-01 11:36:55.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:36:55.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:36:55.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-09-01 11:36:55.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-09-01 11:36:55.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.266
2025-09-01 11:36:55.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.422
2025-09-01 11:36:55.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:36:55.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:36:55.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:36:55.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:36:55.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:36:55.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:36:55.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:36:55.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:36:55.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:37:00.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:37:05.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:37:10.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:37:15.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:37:19.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:37:24.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:37:29.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:37:34.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:37:39.013 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:37:39.013 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 11:37:39.013 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-09-01 11:37:39.013 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:37:39.042 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.98 ms, Average inference time: 7.23 ms

2025-09-01 11:37:39.044 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:37:39.124 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:37:39.213 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch48
2025-09-01 11:37:42.319 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 48/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.970e-03, size: 384, ETA: 3:09:43
2025-09-01 11:37:45.523 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 48/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.969e-03, size: 544, ETA: 3:09:40
2025-09-01 11:37:48.751 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 48/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.3, lr: 1.969e-03, size: 288, ETA: 3:09:37
2025-09-01 11:37:51.846 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 48/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.153s, 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: 1.969e-03, size: 448, ETA: 3:09:32
2025-09-01 11:37:55.111 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 48/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.162s, 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: 1.969e-03, size: 544, ETA: 3:09:29
2025-09-01 11:37:58.397 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 48/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 1.969e-03, size: 256, ETA: 3:09:27
2025-09-01 11:37:59.840 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:38:06.263 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:38:08.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:38:09.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5542
2025-09-01 11:38:09.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4938
2025-09-01 11:38:09.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2865
2025-09-01 11:38:09.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4448
2025-09-01 11:38:09.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:38:09.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:38:09.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.554
2025-09-01 11:38:09.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-09-01 11:38:09.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.286
2025-09-01 11:38:09.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.445
2025-09-01 11:38:09.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:38:09.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:38:09.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:38:09.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:38:09.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:38:09.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:38:09.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:38:09.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:38:09.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:38:11.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:38:12.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:38:14.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:38:15.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:38:16.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:38:18.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:38:19.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:38:21.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:38:22.833 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:38:22.833 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 11:38:22.833 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 11:38:22.833 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:38:22.860 | 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

2025-09-01 11:38:22.862 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:38:22.953 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:38:23.076 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch49
2025-09-01 11:38:26.182 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 49/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.968e-03, size: 416, ETA: 3:09:20
2025-09-01 11:38:29.404 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 49/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.159s, 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: 1.968e-03, size: 256, ETA: 3:09:17
2025-09-01 11:38:32.610 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 49/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.968e-03, size: 320, ETA: 3:09:13
2025-09-01 11:38:35.857 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 49/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.968e-03, size: 480, ETA: 3:09:10
2025-09-01 11:38:39.125 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 49/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, 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: 0.9, lr: 1.968e-03, size: 544, ETA: 3:09:07
2025-09-01 11:38:42.371 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 49/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.2, lr: 1.967e-03, size: 512, ETA: 3:09:04
2025-09-01 11:38:43.905 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:38:50.240 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:38:52.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:38:53.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5396
2025-09-01 11:38:54.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4760
2025-09-01 11:38:54.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2929
2025-09-01 11:38:54.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4362
2025-09-01 11:38:54.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:38:54.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:38:54.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.540
2025-09-01 11:38:54.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-09-01 11:38:54.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.293
2025-09-01 11:38:54.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.436
2025-09-01 11:38:54.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:38:54.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:38:54.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:38:54.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:38:54.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:38:54.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:38:54.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:38:54.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:38:54.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:38:56.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:38:57.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:38:59.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:39:01.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:39:03.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:39:05.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:39:07.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:39:08.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:39:10.682 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:39:10.683 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 11:39:10.683 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 11:39:10.683 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:39:10.708 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.45 ms, Average NMS time: 0.95 ms, Average inference time: 7.40 ms

2025-09-01 11:39:10.710 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:39:10.838 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:39:10.939 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch50
2025-09-01 11:39:13.976 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 50/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.150s, 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: 1.967e-03, size: 256, ETA: 3:08:58
2025-09-01 11:39:17.169 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 50/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 1.967e-03, size: 416, ETA: 3:08:55
2025-09-01 11:39:20.514 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 50/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 8.4, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.0, lr: 1.967e-03, size: 256, ETA: 3:08:53
2025-09-01 11:39:23.835 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 50/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.164s, 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: 1.966e-03, size: 384, ETA: 3:08:51
2025-09-01 11:39:27.004 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 50/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.966e-03, size: 448, ETA: 3:08:47
2025-09-01 11:39:30.188 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 50/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.966e-03, size: 320, ETA: 3:08:43
2025-09-01 11:39:31.631 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:39:37.877 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:39:40.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:39:42.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5608
2025-09-01 11:39:42.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5045
2025-09-01 11:39:43.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3206
2025-09-01 11:39:43.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4620
2025-09-01 11:39:43.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:39:43.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:39:43.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.561
2025-09-01 11:39:43.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-09-01 11:39:43.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.321
2025-09-01 11:39:43.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.462
2025-09-01 11:39:43.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:39:43.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:39:43.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:39:43.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:39:43.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:39:43.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:39:43.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:39:43.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:39:43.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:39:45.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:39:47.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:39:50.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:39:52.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:39:54.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:39:57.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:39:59.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:40:01.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:40:03.801 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:40:03.802 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 11:40:03.802 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 11:40:03.802 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:40:03.831 | 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

2025-09-01 11:40:03.832 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:40:03.925 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:40:04.013 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch51
2025-09-01 11:40:06.988 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 51/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.7, lr: 1.966e-03, size: 288, ETA: 3:08:36
2025-09-01 11:40:10.232 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 51/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.160s, 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: 1.966e-03, size: 384, ETA: 3:08:33
2025-09-01 11:40:13.436 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 51/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.965e-03, size: 416, ETA: 3:08:29
2025-09-01 11:40:16.734 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 51/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.965e-03, size: 288, ETA: 3:08:27
2025-09-01 11:40:20.062 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 51/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.965e-03, size: 576, ETA: 3:08:25
2025-09-01 11:40:23.315 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 51/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.161s, 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: 1.965e-03, size: 288, ETA: 3:08:22
2025-09-01 11:40:24.792 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:40:31.046 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:40:34.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:40:36.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5470
2025-09-01 11:40:37.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4624
2025-09-01 11:40:37.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2901
2025-09-01 11:40:37.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4332
2025-09-01 11:40:37.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:40:37.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:40:37.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.547
2025-09-01 11:40:37.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-09-01 11:40:37.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-09-01 11:40:37.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.433
2025-09-01 11:40:37.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:40:37.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:40:37.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:40:37.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:40:37.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:40:37.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:40:37.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:40:37.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:40:37.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:40:40.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:40:42.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:40:45.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:40:48.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:40:51.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:40:54.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:40:57.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:40:59.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:41:02.579 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:41:02.579 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 11:41:02.579 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 11:41:02.579 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:41:02.605 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.15 ms, Average NMS time: 0.97 ms, Average inference time: 7.12 ms

2025-09-01 11:41:02.606 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:41:02.689 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:41:02.778 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch52
2025-09-01 11:41:05.917 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 52/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.156s, 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: 1.964e-03, size: 576, ETA: 3:08:17
2025-09-01 11:41:09.117 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 52/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.964e-03, size: 256, ETA: 3:08:13
2025-09-01 11:41:12.221 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 52/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.964e-03, size: 480, ETA: 3:08:08
2025-09-01 11:41:15.477 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 52/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.964e-03, size: 256, ETA: 3:08:05
2025-09-01 11:41:18.616 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 52/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.963e-03, size: 384, ETA: 3:08:01
2025-09-01 11:41:21.916 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 52/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, 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: 1.963e-03, size: 352, ETA: 3:07:58
2025-09-01 11:41:23.334 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:41:29.693 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:41:32.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:41:33.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5703
2025-09-01 11:41:34.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5128
2025-09-01 11:41:34.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3198
2025-09-01 11:41:34.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4676
2025-09-01 11:41:34.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:41:34.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:41:34.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.570
2025-09-01 11:41:34.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-09-01 11:41:34.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.320
2025-09-01 11:41:34.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.468
2025-09-01 11:41:34.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:41:34.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:41:34.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:41:34.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:41:34.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:41:34.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:41:34.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:41:34.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:41:34.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:41:36.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:41:38.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:41:40.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:41:42.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:41:44.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:41:46.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:41:48.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:41:49.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:41:51.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:41:51.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 11:41:51.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 11:41:51.954 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:41:51.981 | 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

2025-09-01 11:41:51.982 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:41:52.069 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:41:52.156 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch53
2025-09-01 11:41:55.413 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 53/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.963e-03, size: 576, ETA: 3:07:53
2025-09-01 11:41:58.739 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 53/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, 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: 1.0, lr: 1.963e-03, size: 384, ETA: 3:07:51
2025-09-01 11:42:01.974 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 53/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 1.963e-03, size: 544, ETA: 3:07:48
2025-09-01 11:42:05.298 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 53/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.0, lr: 1.962e-03, size: 576, ETA: 3:07:46
2025-09-01 11:42:08.708 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 53/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.962e-03, size: 512, ETA: 3:07:44
2025-09-01 11:42:12.048 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 53/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.962e-03, size: 512, ETA: 3:07:42
2025-09-01 11:42:13.473 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:42:19.873 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:42:22.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:42:24.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5291
2025-09-01 11:42:24.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4662
2025-09-01 11:42:24.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2875
2025-09-01 11:42:24.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4276
2025-09-01 11:42:24.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:42:24.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:42:24.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-09-01 11:42:24.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-09-01 11:42:24.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.287
2025-09-01 11:42:24.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.428
2025-09-01 11:42:24.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:42:24.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:42:24.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:42:24.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:42:24.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:42:24.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:42:24.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:42:24.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:42:24.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:42:26.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:42:28.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:42:30.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:42:32.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:42:34.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:42:36.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:42:38.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:42:40.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:42:42.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:42:42.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:42:42.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 11:42:42.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:42:42.152 | 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

2025-09-01 11:42:42.154 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:42:42.264 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:42:42.385 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch54
2025-09-01 11:42:45.409 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 54/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.962e-03, size: 288, ETA: 3:07:35
2025-09-01 11:42:48.788 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 54/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.961e-03, size: 384, ETA: 3:07:33
2025-09-01 11:42:52.089 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 54/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.163s, 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: 1.961e-03, size: 448, ETA: 3:07:31
2025-09-01 11:42:55.416 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 54/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.961e-03, size: 288, ETA: 3:07:28
2025-09-01 11:42:58.896 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 54/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.172s, data_time: 0.004s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.961e-03, size: 320, ETA: 3:07:28
2025-09-01 11:43:02.213 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 54/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.163s, 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: 1.960e-03, size: 320, ETA: 3:07:25
2025-09-01 11:43:03.692 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:43:09.831 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:43:12.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:43:13.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5331
2025-09-01 11:43:14.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4773
2025-09-01 11:43:14.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2858
2025-09-01 11:43:14.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4321
2025-09-01 11:43:14.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:43:14.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:43:14.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-09-01 11:43:14.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.477
2025-09-01 11:43:14.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.286
2025-09-01 11:43:14.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.432
2025-09-01 11:43:14.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:43:14.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:43:14.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:43:14.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:43:14.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:43:14.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:43:14.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:43:14.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:43:14.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:43:16.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:43:17.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:43:19.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:43:21.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:43:23.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:43:25.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:43:27.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:43:29.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:43:31.115 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:43:31.115 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 11:43:31.116 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 11:43:31.116 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:43:31.140 | 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: 7.27 ms

2025-09-01 11:43:31.142 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:43:31.223 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:43:31.314 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch55
2025-09-01 11:43:34.469 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 55/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.156s, 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: 1.960e-03, size: 544, ETA: 3:07:20
2025-09-01 11:43:37.702 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 55/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.960e-03, size: 352, ETA: 3:07:17
2025-09-01 11:43:40.901 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 55/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.158s, 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: 1.960e-03, size: 480, ETA: 3:07:13
2025-09-01 11:43:44.189 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 55/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.959e-03, size: 448, ETA: 3:07:11
2025-09-01 11:43:47.470 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 55/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.162s, data_time: 0.007s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.959e-03, size: 544, ETA: 3:07:08
2025-09-01 11:43:50.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 55/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.959e-03, size: 352, ETA: 3:07:04
2025-09-01 11:43:52.100 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:43:58.188 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:44:02.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:44:04.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5416
2025-09-01 11:44:05.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5082
2025-09-01 11:44:05.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2861
2025-09-01 11:44:05.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4453
2025-09-01 11:44:05.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:44:05.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:44:05.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-09-01 11:44:05.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.508
2025-09-01 11:44:05.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.286
2025-09-01 11:44:05.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.445
2025-09-01 11:44:05.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:44:05.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:44:05.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:44:05.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:44:05.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:44:05.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:44:05.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:44:05.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:44:05.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:44:08.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:44:11.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:44:14.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:44:18.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:44:21.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:44:24.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:44:27.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:44:30.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:44:34.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:44:34.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 11:44:34.052 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 11:44:34.053 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:44:34.081 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.95 ms, Average inference time: 7.04 ms

2025-09-01 11:44:34.084 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:44:34.170 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:44:34.315 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch56
2025-09-01 11:44:37.501 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 56/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.0, lr: 1.959e-03, size: 448, ETA: 3:06:59
2025-09-01 11:44:40.761 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 56/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.1, lr: 1.958e-03, size: 384, ETA: 3:06:56
2025-09-01 11:44:43.938 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 56/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 9.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.7, cls_loss: 1.1, lr: 1.958e-03, size: 576, ETA: 3:06:52
2025-09-01 11:44:47.220 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 56/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.7Gb, 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: 1.958e-03, size: 512, ETA: 3:06:50
2025-09-01 11:44:50.451 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 56/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.958e-03, size: 320, ETA: 3:06:46
2025-09-01 11:44:53.577 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 56/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.957e-03, size: 352, ETA: 3:06:42
2025-09-01 11:44:55.080 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:45:01.381 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:45:03.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:45:05.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5565
2025-09-01 11:45:05.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4806
2025-09-01 11:45:05.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2536
2025-09-01 11:45:05.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4303
2025-09-01 11:45:05.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:45:05.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:45:05.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.557
2025-09-01 11:45:05.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-09-01 11:45:05.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.254
2025-09-01 11:45:05.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.430
2025-09-01 11:45:05.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:45:05.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:45:05.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:45:05.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:45:05.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:45:05.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:45:05.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:45:05.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:45:05.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:45:08.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:45:10.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:45:12.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:45:14.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:45:16.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:45:18.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:45:20.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:45:23.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:45:25.072 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:45:25.072 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 11:45:25.073 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 11:45:25.073 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:45:25.100 | 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: 7.20 ms

2025-09-01 11:45:25.101 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:45:25.190 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:45:25.281 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch57
2025-09-01 11:45:28.597 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 57/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.957e-03, size: 352, ETA: 3:06:39
2025-09-01 11:45:31.942 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 57/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.164s, 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: 1.957e-03, size: 512, ETA: 3:06:36
2025-09-01 11:45:35.280 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 57/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.165s, 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: 1.957e-03, size: 512, ETA: 3:06:34
2025-09-01 11:45:38.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 57/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.156s, 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: 1.956e-03, size: 352, ETA: 3:06:30
2025-09-01 11:45:41.743 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 57/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.7Gb, iter_time: 0.163s, data_time: 0.005s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.956e-03, size: 480, ETA: 3:06:28
2025-09-01 11:45:44.998 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 57/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.161s, 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: 1.956e-03, size: 320, ETA: 3:06:25
2025-09-01 11:45:46.418 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:45:52.672 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:45:55.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:45:56.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5297
2025-09-01 11:45:56.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4831
2025-09-01 11:45:57.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3096
2025-09-01 11:45:57.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4408
2025-09-01 11:45:57.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:45:57.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:45:57.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-09-01 11:45:57.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.483
2025-09-01 11:45:57.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.310
2025-09-01 11:45:57.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.441
2025-09-01 11:45:57.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:45:57.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:45:57.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:45:57.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:45:57.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:45:57.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:45:57.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:45:57.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:45:57.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:45:59.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:46:01.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:46:04.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:46:06.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:46:08.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:46:10.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:46:13.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:46:15.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:46:17.882 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:46:17.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:46:17.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 11:46:17.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:46:17.914 | 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

2025-09-01 11:46:17.915 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:46:18.033 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:46:18.128 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch58
2025-09-01 11:46:21.224 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 58/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.956e-03, size: 448, ETA: 3:06:19
2025-09-01 11:46:24.418 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 58/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.955e-03, size: 512, ETA: 3:06:15
2025-09-01 11:46:27.617 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 58/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.7Gb, 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: 1.955e-03, size: 384, ETA: 3:06:11
2025-09-01 11:46:30.770 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 58/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.9, lr: 1.955e-03, size: 256, ETA: 3:06:07
2025-09-01 11:46:34.077 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 58/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.955e-03, size: 320, ETA: 3:06:05
2025-09-01 11:46:37.235 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 58/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, 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: 1.954e-03, size: 352, ETA: 3:06:01
2025-09-01 11:46:38.691 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:46:44.904 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:46:46.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:46:48.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5461
2025-09-01 11:46:48.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4914
2025-09-01 11:46:48.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3075
2025-09-01 11:46:48.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4483
2025-09-01 11:46:48.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:46:48.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:46:48.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.546
2025-09-01 11:46:48.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-09-01 11:46:48.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.308
2025-09-01 11:46:48.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.448
2025-09-01 11:46:48.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:46:48.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:46:48.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:46:48.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:46:48.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:46:48.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:46:48.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:46:48.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:46:48.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:46:50.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:46:52.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:46:54.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:46:56.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:46:57.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:46:59.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:47:01.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:47:02.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:47:04.693 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:47:04.694 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 11:47:04.694 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 11:47:04.694 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:47:04.720 | 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

2025-09-01 11:47:04.721 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:47:04.848 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:47:04.939 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch59
2025-09-01 11:47:08.069 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 59/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.154s, 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: 1.954e-03, size: 544, ETA: 3:05:55
2025-09-01 11:47:11.294 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 59/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.954e-03, size: 256, ETA: 3:05:52
2025-09-01 11:47:14.585 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 59/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.954e-03, size: 576, ETA: 3:05:49
2025-09-01 11:47:17.884 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 59/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.953e-03, size: 480, ETA: 3:05:47
2025-09-01 11:47:21.080 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 59/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, 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: 1.1, lr: 1.953e-03, size: 384, ETA: 3:05:43
2025-09-01 11:47:24.235 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 59/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.953e-03, size: 416, ETA: 3:05:39
2025-09-01 11:47:25.671 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:47:31.898 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:47:35.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:47:37.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5397
2025-09-01 11:47:37.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4977
2025-09-01 11:47:37.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3012
2025-09-01 11:47:37.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4462
2025-09-01 11:47:37.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:47:37.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:47:37.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.540
2025-09-01 11:47:37.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-09-01 11:47:37.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.301
2025-09-01 11:47:37.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.446
2025-09-01 11:47:37.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:47:37.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:47:37.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:47:37.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:47:37.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:47:37.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:47:37.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:47:37.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:47:37.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:47:40.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:47:43.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:47:45.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:47:48.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:47:51.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:47:53.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:47:56.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:47:59.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:48:02.601 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:48:02.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 11:48:02.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 11:48:02.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:48:02.630 | 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: 7.26 ms

2025-09-01 11:48:02.631 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:48:02.718 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:48:02.806 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch60
2025-09-01 11:48:05.851 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 60/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.150s, 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: 1.952e-03, size: 544, ETA: 3:05:32
2025-09-01 11:48:09.104 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 60/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.160s, 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: 1.952e-03, size: 480, ETA: 3:05:29
2025-09-01 11:48:12.422 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 60/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.163s, 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: 1.952e-03, size: 352, ETA: 3:05:27
2025-09-01 11:48:15.608 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 60/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.952e-03, size: 320, ETA: 3:05:23
2025-09-01 11:48:18.802 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 60/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.951e-03, size: 576, ETA: 3:05:20
2025-09-01 11:48:22.274 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 60/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.172s, 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: 1.951e-03, size: 320, ETA: 3:05:19
2025-09-01 11:48:23.669 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:48:29.926 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:48:33.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:48:35.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5327
2025-09-01 11:48:35.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4988
2025-09-01 11:48:35.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2875
2025-09-01 11:48:35.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4397
2025-09-01 11:48:35.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:48:35.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:48:35.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-09-01 11:48:35.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-09-01 11:48:35.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.288
2025-09-01 11:48:35.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.440
2025-09-01 11:48:35.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:48:35.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:48:35.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:48:35.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:48:35.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:48:35.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:48:35.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:48:35.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:48:35.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:48:38.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:48:41.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:48:44.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:48:46.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:48:49.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:48:52.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:48:54.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:48:57.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:49:00.032 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:49:00.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-09-01 11:49:00.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 11:49:00.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:49:00.061 | 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

2025-09-01 11:49:00.062 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:49:00.200 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:49:00.290 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch61
2025-09-01 11:49:03.474 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 61/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.1, lr: 1.951e-03, size: 448, ETA: 3:05:13
2025-09-01 11:49:07.018 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 61/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.175s, 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: 1.951e-03, size: 512, ETA: 3:05:13
2025-09-01 11:49:10.473 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 61/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.171s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.950e-03, size: 448, ETA: 3:05:11
2025-09-01 11:49:13.824 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 61/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, 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: 1.0, lr: 1.950e-03, size: 448, ETA: 3:05:09
2025-09-01 11:49:17.161 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 61/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.164s, data_time: 0.004s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.950e-03, size: 384, ETA: 3:05:07
2025-09-01 11:49:20.753 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 61/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.177s, data_time: 0.003s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.950e-03, size: 416, ETA: 3:05:07
2025-09-01 11:49:22.166 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:49:28.685 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:49:32.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:49:34.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5265
2025-09-01 11:49:34.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4404
2025-09-01 11:49:35.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2634
2025-09-01 11:49:35.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4101
2025-09-01 11:49:35.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:49:35.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:49:35.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-09-01 11:49:35.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.440
2025-09-01 11:49:35.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.263
2025-09-01 11:49:35.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.410
2025-09-01 11:49:35.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:49:35.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:49:35.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:49:35.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:49:35.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:49:35.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:49:35.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:49:35.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:49:35.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:49:38.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:49:41.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:49:44.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:49:47.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:49:49.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:49:52.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:49:55.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:49:58.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:50:01.581 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:50:01.582 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-09-01 11:50:01.582 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-09-01 11:50:01.582 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:50:01.607 | 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

2025-09-01 11:50:01.613 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:50:01.688 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:50:01.776 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch62
2025-09-01 11:50:04.948 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 62/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.949e-03, size: 544, ETA: 3:05:01
2025-09-01 11:50:08.264 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 62/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.949e-03, size: 576, ETA: 3:04:59
2025-09-01 11:50:11.533 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 62/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.949e-03, size: 448, ETA: 3:04:56
2025-09-01 11:50:14.741 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 62/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.158s, 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: 1.948e-03, size: 288, ETA: 3:04:52
2025-09-01 11:50:18.022 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 62/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.948e-03, size: 352, ETA: 3:04:49
2025-09-01 11:50:21.365 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 62/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.164s, 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: 1.948e-03, size: 448, ETA: 3:04:47
2025-09-01 11:50:22.933 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:50:29.303 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:50:31.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:50:33.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5497
2025-09-01 11:50:33.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4848
2025-09-01 11:50:33.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2889
2025-09-01 11:50:33.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4411
2025-09-01 11:50:33.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:50:33.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:50:33.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.550
2025-09-01 11:50:33.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-09-01 11:50:33.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.289
2025-09-01 11:50:33.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.441
2025-09-01 11:50:33.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:50:33.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:50:33.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:50:33.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:50:33.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:50:33.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:50:33.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:50:33.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:50:33.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:50:35.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:50:37.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:50:39.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:50:41.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:50:43.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:50:45.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:50:47.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:50:49.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:50:51.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:50:51.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 11:50:51.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 11:50:51.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:50:51.736 | 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: 7.22 ms

2025-09-01 11:50:51.738 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:50:51.863 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:50:51.963 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch63
2025-09-01 11:50:55.105 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 63/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.156s, 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: 1.948e-03, size: 352, ETA: 3:04:43
2025-09-01 11:50:58.321 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 63/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.158s, 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: 1.947e-03, size: 512, ETA: 3:04:39
2025-09-01 11:51:01.550 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 63/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.8, lr: 1.947e-03, size: 416, ETA: 3:04:36
2025-09-01 11:51:04.836 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 63/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.162s, 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: 1.947e-03, size: 512, ETA: 3:04:33
2025-09-01 11:51:08.122 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 63/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.946e-03, size: 480, ETA: 3:04:30
2025-09-01 11:51:11.497 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 63/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.946e-03, size: 320, ETA: 3:04:29
2025-09-01 11:51:12.949 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:51:19.501 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:51:21.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:51:22.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5278
2025-09-01 11:51:22.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4678
2025-09-01 11:51:22.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3043
2025-09-01 11:51:22.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4333
2025-09-01 11:51:22.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:51:22.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:51:22.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-09-01 11:51:22.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-09-01 11:51:22.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-09-01 11:51:22.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.433
2025-09-01 11:51:22.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:51:22.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:51:22.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:51:22.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:51:22.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:51:22.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:51:22.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:51:22.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:51:22.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:51:24.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:51:25.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:51:27.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:51:29.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:51:30.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:51:32.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:51:33.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:51:35.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:51:36.607 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:51:36.607 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 11:51:36.607 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 11:51:36.608 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:51:36.636 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.99 ms, Average inference time: 7.26 ms

2025-09-01 11:51:36.637 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:51:36.776 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:51:36.864 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch64
2025-09-01 11:51:39.995 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 64/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.946e-03, size: 288, ETA: 3:04:23
2025-09-01 11:51:43.234 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 64/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.946e-03, size: 480, ETA: 3:04:20
2025-09-01 11:51:46.456 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 64/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, 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: 0.9, lr: 1.945e-03, size: 256, ETA: 3:04:16
2025-09-01 11:51:49.580 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 64/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.5, cls_loss: 1.1, lr: 1.945e-03, size: 512, ETA: 3:04:12
2025-09-01 11:51:52.881 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 64/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 7.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.945e-03, size: 512, ETA: 3:04:10
2025-09-01 11:51:56.041 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 64/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.0, lr: 1.944e-03, size: 352, ETA: 3:04:06
2025-09-01 11:51:57.479 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:52:03.787 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:52:05.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:52:07.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5585
2025-09-01 11:52:07.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5039
2025-09-01 11:52:07.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3009
2025-09-01 11:52:07.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4544
2025-09-01 11:52:07.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:52:07.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:52:07.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.558
2025-09-01 11:52:07.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-09-01 11:52:07.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.301
2025-09-01 11:52:07.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.454
2025-09-01 11:52:07.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:52:07.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:52:07.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:52:07.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:52:07.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:52:07.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:52:07.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:52:07.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:52:07.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:52:09.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:52:10.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:52:12.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:52:14.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:52:16.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:52:17.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:52:19.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:52:21.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:52:22.786 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:52:22.787 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:52:22.787 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 11:52:22.787 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:52:22.815 | 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

2025-09-01 11:52:22.816 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:52:22.938 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:52:23.077 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch65
2025-09-01 11:52:26.166 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 65/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.944e-03, size: 416, ETA: 3:04:00
2025-09-01 11:52:29.400 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 65/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.944e-03, size: 256, ETA: 3:03:57
2025-09-01 11:52:32.645 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 65/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.944e-03, size: 448, ETA: 3:03:54
2025-09-01 11:52:35.827 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 65/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.943e-03, size: 320, ETA: 3:03:50
2025-09-01 11:52:39.085 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 65/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.162s, 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: 1.943e-03, size: 352, ETA: 3:03:47
2025-09-01 11:52:42.329 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 65/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.160s, 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: 1.943e-03, size: 256, ETA: 3:03:44
2025-09-01 11:52:43.884 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:52:50.148 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:52:51.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:52:52.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4963
2025-09-01 11:52:53.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4372
2025-09-01 11:52:53.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2444
2025-09-01 11:52:53.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3926
2025-09-01 11:52:53.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:52:53.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:52:53.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-09-01 11:52:53.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.437
2025-09-01 11:52:53.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.244
2025-09-01 11:52:53.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.393
2025-09-01 11:52:53.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:52:53.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:52:53.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:52:53.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:52:53.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:52:53.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:52:53.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:52:53.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:52:53.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:52:54.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:52:55.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:52:57.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:52:58.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:52:59.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:53:01.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:53:02.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:53:03.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:53:05.299 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:53:05.299 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-09-01 11:53:05.299 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-09-01 11:53:05.299 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:53:05.307 | 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: 7.13 ms

2025-09-01 11:53:05.308 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:53:05.382 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:53:05.468 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch66
2025-09-01 11:53:08.721 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 66/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.942e-03, size: 576, ETA: 3:03:40
2025-09-01 11:53:12.005 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 66/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.942e-03, size: 416, ETA: 3:03:37
2025-09-01 11:53:15.342 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 66/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.942e-03, size: 320, ETA: 3:03:35
2025-09-01 11:53:18.625 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 66/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.942e-03, size: 448, ETA: 3:03:32
2025-09-01 11:53:21.887 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 66/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.941e-03, size: 480, ETA: 3:03:29
2025-09-01 11:53:25.160 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 66/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.0, lr: 1.941e-03, size: 384, ETA: 3:03:26
2025-09-01 11:53:26.619 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:53:32.995 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:53:35.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:53:37.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5425
2025-09-01 11:53:37.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4973
2025-09-01 11:53:37.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3119
2025-09-01 11:53:37.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4506
2025-09-01 11:53:37.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:53:37.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:53:37.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-09-01 11:53:37.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-09-01 11:53:37.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-09-01 11:53:37.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.451
2025-09-01 11:53:37.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:53:37.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:53:37.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:53:37.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:53:37.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:53:37.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:53:37.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:53:37.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:53:37.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:53:39.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:53:42.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:53:44.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:53:46.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:53:48.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:53:50.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:53:52.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:53:54.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:53:56.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:53:56.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:53:56.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 11:53:56.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:53:56.986 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.06 ms, Average NMS time: 0.96 ms, Average inference time: 7.02 ms

2025-09-01 11:53:56.990 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:53:57.121 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:53:57.207 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch67
2025-09-01 11:54:00.358 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 67/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.941e-03, size: 480, ETA: 3:03:21
2025-09-01 11:54:03.532 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 67/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.156s, 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: 1.940e-03, size: 288, ETA: 3:03:17
2025-09-01 11:54:06.761 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 67/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.940e-03, size: 512, ETA: 3:03:14
2025-09-01 11:54:10.049 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 67/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.163s, 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: 1.940e-03, size: 384, ETA: 3:03:11
2025-09-01 11:54:13.242 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 67/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.939e-03, size: 512, ETA: 3:03:07
2025-09-01 11:54:16.585 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 67/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.165s, 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: 1.939e-03, size: 320, ETA: 3:03:05
2025-09-01 11:54:18.051 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:54:24.231 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:54:26.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:54:28.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5180
2025-09-01 11:54:28.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5084
2025-09-01 11:54:28.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2628
2025-09-01 11:54:28.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4298
2025-09-01 11:54:28.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:54:28.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:54:28.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.518
2025-09-01 11:54:28.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.508
2025-09-01 11:54:28.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.263
2025-09-01 11:54:28.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.430
2025-09-01 11:54:28.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:54:28.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:54:28.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:54:28.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:54:28.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:54:28.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:54:28.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:54:28.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:54:28.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:54:30.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:54:32.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:54:34.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:54:36.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:54:38.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:54:40.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:54:43.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:54:45.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:54:47.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:54:47.106 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:54:47.106 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 11:54:47.106 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:54:47.134 | 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

2025-09-01 11:54:47.135 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:54:47.213 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:54:47.302 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch68
2025-09-01 11:54:50.380 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 68/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.939e-03, size: 320, ETA: 3:02:59
2025-09-01 11:54:53.643 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 68/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, 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: 0.8, lr: 1.939e-03, size: 320, ETA: 3:02:56
2025-09-01 11:54:56.923 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 68/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.162s, 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: 1.938e-03, size: 288, ETA: 3:02:54
2025-09-01 11:55:00.041 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 68/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.938e-03, size: 544, ETA: 3:02:49
2025-09-01 11:55:03.456 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 68/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.168s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.938e-03, size: 576, ETA: 3:02:47
2025-09-01 11:55:06.777 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 68/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.163s, 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: 1.937e-03, size: 384, ETA: 3:02:45
2025-09-01 11:55:08.273 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:55:14.439 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:55:16.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:55:18.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5365
2025-09-01 11:55:19.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4862
2025-09-01 11:55:19.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2829
2025-09-01 11:55:19.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4352
2025-09-01 11:55:19.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:55:19.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:55:19.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-09-01 11:55:19.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-09-01 11:55:19.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.283
2025-09-01 11:55:19.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.435
2025-09-01 11:55:19.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:55:19.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:55:19.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:55:19.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:55:19.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:55:19.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:55:19.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:55:19.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:55:19.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:55:21.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:55:23.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:55:25.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:55:28.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:55:30.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:55:32.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:55:34.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:55:37.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:55:39.265 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:55:39.266 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 11:55:39.266 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 11:55:39.266 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:55:39.293 | 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

2025-09-01 11:55:39.294 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:55:39.373 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:55:39.465 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch69
2025-09-01 11:55:42.611 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 69/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.156s, 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: 1.937e-03, size: 512, ETA: 3:02:40
2025-09-01 11:55:46.039 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 69/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.169s, 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: 1.937e-03, size: 576, ETA: 3:02:38
2025-09-01 11:55:49.469 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 69/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.936e-03, size: 480, ETA: 3:02:36
2025-09-01 11:55:52.789 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 69/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.936e-03, size: 416, ETA: 3:02:34
2025-09-01 11:55:56.038 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 69/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.160s, 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: 1.936e-03, size: 448, ETA: 3:02:30
2025-09-01 11:55:59.369 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 69/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.9, lr: 1.936e-03, size: 512, ETA: 3:02:28
2025-09-01 11:56:00.801 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:56:07.275 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:56:09.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:56:10.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5541
2025-09-01 11:56:11.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4861
2025-09-01 11:56:11.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3014
2025-09-01 11:56:11.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4472
2025-09-01 11:56:11.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:56:11.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:56:11.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.554
2025-09-01 11:56:11.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-09-01 11:56:11.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.301
2025-09-01 11:56:11.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.447
2025-09-01 11:56:11.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:56:11.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:56:11.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:56:11.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:56:11.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:56:11.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:56:11.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:56:11.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:56:11.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:56:12.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:56:14.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:56:16.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:56:18.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:56:19.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:56:21.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:56:23.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:56:25.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:56:26.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:56:26.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 11:56:26.805 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 11:56:26.805 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:56:26.830 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.95 ms, Average inference time: 7.03 ms

2025-09-01 11:56:26.832 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:56:26.912 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:56:26.997 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch70
2025-09-01 11:56:30.092 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 70/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.152s, 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: 1.935e-03, size: 416, ETA: 3:02:22
2025-09-01 11:56:33.368 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 70/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.161s, 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: 1.935e-03, size: 448, ETA: 3:02:19
2025-09-01 11:56:36.754 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 70/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.3, cls_loss: 1.1, lr: 1.935e-03, size: 512, ETA: 3:02:17
2025-09-01 11:56:40.066 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 70/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.165s, 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: 1.934e-03, size: 416, ETA: 3:02:14
2025-09-01 11:56:43.336 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 70/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.162s, 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: 1.934e-03, size: 416, ETA: 3:02:11
2025-09-01 11:56:46.682 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 70/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.934e-03, size: 448, ETA: 3:02:09
2025-09-01 11:56:48.230 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:56:54.604 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:56:57.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:56:59.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5449
2025-09-01 11:56:59.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4785
2025-09-01 11:56:59.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2676
2025-09-01 11:56:59.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4303
2025-09-01 11:56:59.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:56:59.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:56:59.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-09-01 11:56:59.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.478
2025-09-01 11:56:59.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.268
2025-09-01 11:56:59.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.430
2025-09-01 11:56:59.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:56:59.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:56:59.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:56:59.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:56:59.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:56:59.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:56:59.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:56:59.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:56:59.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:57:02.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:57:04.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:57:07.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:57:09.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:57:11.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:57:13.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:57:15.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:57:17.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:57:20.134 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:57:20.134 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 11:57:20.134 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 11:57:20.134 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:57:20.162 | 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

2025-09-01 11:57:20.163 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:57:20.242 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:57:20.327 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch71
2025-09-01 11:57:23.418 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 71/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.933e-03, size: 384, ETA: 3:02:04
2025-09-01 11:57:26.808 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 71/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.168s, 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: 1.933e-03, size: 576, ETA: 3:02:02
2025-09-01 11:57:30.034 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 71/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.933e-03, size: 256, ETA: 3:01:59
2025-09-01 11:57:33.389 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 71/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.166s, 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: 1.932e-03, size: 576, ETA: 3:01:56
2025-09-01 11:57:36.668 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 71/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.932e-03, size: 352, ETA: 3:01:53
2025-09-01 11:57:39.843 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 71/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.1, lr: 1.932e-03, size: 288, ETA: 3:01:50
2025-09-01 11:57:41.237 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:57:47.609 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:57:49.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:57:50.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5046
2025-09-01 11:57:50.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4176
2025-09-01 11:57:50.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2275
2025-09-01 11:57:50.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3833
2025-09-01 11:57:50.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:57:50.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:57:50.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-09-01 11:57:50.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.418
2025-09-01 11:57:50.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.228
2025-09-01 11:57:50.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.383
2025-09-01 11:57:50.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:57:50.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:57:50.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:57:50.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:57:50.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:57:50.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:57:50.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:57:50.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:57:50.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:57:51.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:57:52.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:57:53.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:57:55.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:57:56.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:57:57.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:57:58.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:57:59.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:58:00.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:58:00.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-09-01 11:58:00.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-09-01 11:58:00.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:58:00.966 | 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

2025-09-01 11:58:00.967 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:58:01.043 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:58:01.182 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch72
2025-09-01 11:58:04.213 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 72/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.931e-03, size: 320, ETA: 3:01:43
2025-09-01 11:58:07.333 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 72/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.9, lr: 1.931e-03, size: 288, ETA: 3:01:39
2025-09-01 11:58:10.581 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 72/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, 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: 1.931e-03, size: 480, ETA: 3:01:36
2025-09-01 11:58:13.788 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 72/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.158s, 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: 1.931e-03, size: 448, ETA: 3:01:33
2025-09-01 11:58:17.103 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 72/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.930e-03, size: 576, ETA: 3:01:30
2025-09-01 11:58:20.372 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 72/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.930e-03, size: 416, ETA: 3:01:27
2025-09-01 11:58:21.863 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:58:28.108 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:58:30.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:58:31.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5589
2025-09-01 11:58:32.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4941
2025-09-01 11:58:32.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3095
2025-09-01 11:58:32.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4542
2025-09-01 11:58:32.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:58:32.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:58:32.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-09-01 11:58:32.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-09-01 11:58:32.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.309
2025-09-01 11:58:32.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.454
2025-09-01 11:58:32.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:58:32.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:58:32.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:58:32.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:58:32.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:58:32.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:58:32.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:58:32.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:58:32.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:58:33.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:58:35.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:58:37.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:58:39.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:58:41.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:58:43.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:58:45.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:58:46.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:58:48.657 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:58:48.657 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 11:58:48.657 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 11:58:48.657 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:58:48.684 | 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: 7.15 ms

2025-09-01 11:58:48.688 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:58:48.773 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:58:48.864 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch73
2025-09-01 11:58:52.015 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 73/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.155s, 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: 1.929e-03, size: 256, ETA: 3:01:22
2025-09-01 11:58:55.293 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 73/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.161s, 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: 1.929e-03, size: 320, ETA: 3:01:19
2025-09-01 11:58:58.482 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 73/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.929e-03, size: 448, ETA: 3:01:15
2025-09-01 11:59:01.899 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 73/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.169s, 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: 1.929e-03, size: 512, ETA: 3:01:14
2025-09-01 11:59:05.362 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 73/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.170s, 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: 1.928e-03, size: 480, ETA: 3:01:12
2025-09-01 11:59:08.797 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 73/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.170s, 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: 1.928e-03, size: 448, ETA: 3:01:10
2025-09-01 11:59:10.190 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:59:16.539 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 11:59:18.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 11:59:20.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5280
2025-09-01 11:59:20.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4883
2025-09-01 11:59:20.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3054
2025-09-01 11:59:20.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4406
2025-09-01 11:59:20.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 11:59:20.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 11:59:20.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-09-01 11:59:20.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-09-01 11:59:20.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.305
2025-09-01 11:59:20.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.441
2025-09-01 11:59:20.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 11:59:20.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 11:59:20.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 11:59:20.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 11:59:20.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 11:59:20.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 11:59:20.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 11:59:20.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 11:59:20.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 11:59:22.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 11:59:24.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 11:59:26.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 11:59:28.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 11:59:30.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 11:59:32.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 11:59:34.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 11:59:36.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 11:59:38.338 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 11:59:38.338 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-09-01 11:59:38.339 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 11:59:38.339 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 11:59:38.364 | 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

2025-09-01 11:59:38.365 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:59:38.542 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 11:59:38.670 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch74
2025-09-01 11:59:41.719 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 74/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.928e-03, size: 384, ETA: 3:01:04
2025-09-01 11:59:44.896 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 74/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.927e-03, size: 416, ETA: 3:01:00
2025-09-01 11:59:48.363 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 74/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.171s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.2, lr: 1.927e-03, size: 288, ETA: 3:00:59
2025-09-01 11:59:51.599 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 74/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.159s, 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: 1.927e-03, size: 320, ETA: 3:00:55
2025-09-01 11:59:55.085 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 74/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.172s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 1.1, lr: 1.926e-03, size: 352, ETA: 3:00:54
2025-09-01 11:59:58.395 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 74/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.163s, 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: 1.926e-03, size: 576, ETA: 3:00:51
2025-09-01 11:59:59.905 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:00:06.238 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:00:09.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:00:11.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5535
2025-09-01 12:00:11.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4965
2025-09-01 12:00:11.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2920
2025-09-01 12:00:11.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4473
2025-09-01 12:00:11.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:00:11.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:00:11.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.553
2025-09-01 12:00:11.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-09-01 12:00:11.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.292
2025-09-01 12:00:11.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.447
2025-09-01 12:00:11.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:00:11.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:00:11.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:00:11.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:00:11.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:00:11.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:00:11.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:00:11.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:00:11.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:00:14.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:00:17.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:00:19.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:00:22.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:00:24.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:00:27.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:00:29.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:00:32.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:00:34.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:00:34.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:00:34.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:00:34.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:00:35.028 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.07 ms, Average NMS time: 0.98 ms, Average inference time: 7.05 ms

2025-09-01 12:00:35.029 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:00:35.119 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:00:35.211 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch75
2025-09-01 12:00:38.453 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 75/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.159s, 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: 1.926e-03, size: 352, ETA: 3:00:47
2025-09-01 12:00:41.698 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 75/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.925e-03, size: 512, ETA: 3:00:44
2025-09-01 12:00:44.923 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 75/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.159s, 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: 1.925e-03, size: 448, ETA: 3:00:40
2025-09-01 12:00:48.203 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 75/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.925e-03, size: 384, ETA: 3:00:37
2025-09-01 12:00:51.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 75/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.924e-03, size: 480, ETA: 3:00:34
2025-09-01 12:00:54.722 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 75/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.162s, 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: 1.924e-03, size: 384, ETA: 3:00:31
2025-09-01 12:00:56.163 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:01:02.742 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:01:04.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:01:06.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5399
2025-09-01 12:01:06.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4623
2025-09-01 12:01:06.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2378
2025-09-01 12:01:06.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4134
2025-09-01 12:01:06.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:01:06.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:01:06.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.540
2025-09-01 12:01:06.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.462
2025-09-01 12:01:06.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.238
2025-09-01 12:01:06.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.413
2025-09-01 12:01:06.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:01:06.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:01:06.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:01:06.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:01:06.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:01:06.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:01:06.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:01:06.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:01:06.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:01:08.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:01:10.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:01:12.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:01:14.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:01:16.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:01:17.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:01:19.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:01:21.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:01:23.349 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:01:23.349 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:01:23.350 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-09-01 12:01:23.350 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:01:23.377 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.90 ms, Average inference time: 7.20 ms

2025-09-01 12:01:23.378 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:01:23.466 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:01:23.612 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch76
2025-09-01 12:01:26.719 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 76/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.924e-03, size: 480, ETA: 3:00:26
2025-09-01 12:01:30.044 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 76/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.163s, 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: 1.923e-03, size: 544, ETA: 3:00:23
2025-09-01 12:01:33.203 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 76/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.923e-03, size: 480, ETA: 3:00:19
2025-09-01 12:01:36.478 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 76/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.161s, 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: 1.923e-03, size: 544, ETA: 3:00:16
2025-09-01 12:01:39.805 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 76/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.922e-03, size: 256, ETA: 3:00:14
2025-09-01 12:01:43.210 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 76/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.922e-03, size: 352, ETA: 3:00:12
2025-09-01 12:01:44.632 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:01:50.813 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:01:53.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:01:55.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5510
2025-09-01 12:01:55.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4892
2025-09-01 12:01:55.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2504
2025-09-01 12:01:55.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4302
2025-09-01 12:01:55.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:01:55.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:01:55.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-09-01 12:01:55.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-09-01 12:01:55.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.250
2025-09-01 12:01:55.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.430
2025-09-01 12:01:55.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:01:55.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:01:55.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:01:55.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:01:55.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:01:55.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:01:55.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:01:55.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:01:55.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:01:58.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:02:00.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:02:02.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:02:05.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:02:07.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:02:09.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:02:11.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:02:14.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:02:16.437 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:02:16.437 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:02:16.437 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 12:02:16.437 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:02:16.466 | 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: 7.18 ms

2025-09-01 12:02:16.467 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:02:16.553 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:02:16.640 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch77
2025-09-01 12:02:19.857 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 77/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.922e-03, size: 512, ETA: 3:00:06
2025-09-01 12:02:23.246 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 77/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.168s, 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: 1.921e-03, size: 288, ETA: 3:00:04
2025-09-01 12:02:26.391 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 77/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.921e-03, size: 352, ETA: 3:00:00
2025-09-01 12:02:29.642 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 77/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.921e-03, size: 544, ETA: 2:59:57
2025-09-01 12:02:32.826 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 77/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.156s, 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: 1.920e-03, size: 352, ETA: 2:59:53
2025-09-01 12:02:36.124 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 77/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.163s, 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: 1.920e-03, size: 544, ETA: 2:59:51
2025-09-01 12:02:37.577 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:02:44.135 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:02:47.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:02:50.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5091
2025-09-01 12:02:50.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4039
2025-09-01 12:02:51.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2637
2025-09-01 12:02:51.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3922
2025-09-01 12:02:51.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:02:51.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:02:51.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-09-01 12:02:51.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.404
2025-09-01 12:02:51.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.264
2025-09-01 12:02:51.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.392
2025-09-01 12:02:51.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:02:51.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:02:51.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:02:51.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:02:51.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:02:51.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:02:51.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:02:51.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:02:51.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:02:54.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:02:57.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:03:00.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:03:03.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:03:06.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:03:09.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:03:12.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:03:15.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:03:18.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:03:18.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-09-01 12:03:18.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-09-01 12:03:18.459 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:03:18.486 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 0.98 ms, Average inference time: 7.21 ms

2025-09-01 12:03:18.487 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:03:18.609 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:03:18.701 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch78
2025-09-01 12:03:21.755 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 78/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.1, lr: 1.920e-03, size: 512, ETA: 2:59:45
2025-09-01 12:03:25.052 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 78/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.163s, 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: 1.919e-03, size: 288, ETA: 2:59:42
2025-09-01 12:03:28.178 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 78/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.154s, 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: 1.919e-03, size: 256, ETA: 2:59:38
2025-09-01 12:03:31.448 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 78/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.161s, data_time: 0.006s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.919e-03, size: 480, ETA: 2:59:35
2025-09-01 12:03:34.713 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 78/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.160s, 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: 1.918e-03, size: 320, ETA: 2:59:32
2025-09-01 12:03:38.023 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 78/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.918e-03, size: 448, ETA: 2:59:29
2025-09-01 12:03:39.569 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:03:45.751 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:03:48.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:03:50.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5633
2025-09-01 12:03:50.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4978
2025-09-01 12:03:50.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3273
2025-09-01 12:03:50.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4628
2025-09-01 12:03:50.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:03:50.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:03:50.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.563
2025-09-01 12:03:50.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-09-01 12:03:50.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.327
2025-09-01 12:03:50.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.463
2025-09-01 12:03:50.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:03:50.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:03:50.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:03:50.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:03:50.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:03:50.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:03:50.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:03:50.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:03:50.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:03:52.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:03:54.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:03:56.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:03:59.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:04:01.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:04:03.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:04:05.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:04:07.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:04:09.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:04:09.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 12:04:09.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 12:04:09.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:04:10.024 | 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

2025-09-01 12:04:10.025 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:04:10.109 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:04:10.196 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch79
2025-09-01 12:04:13.282 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 79/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.917e-03, size: 480, ETA: 2:59:24
2025-09-01 12:04:16.535 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 79/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.160s, 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: 1.917e-03, size: 352, ETA: 2:59:21
2025-09-01 12:04:19.678 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 79/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.156s, 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: 1.917e-03, size: 320, ETA: 2:59:17
2025-09-01 12:04:22.905 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 79/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.8, l1_loss: 0.0, conf_loss: 4.5, cls_loss: 0.6, lr: 1.916e-03, size: 416, ETA: 2:59:14
2025-09-01 12:04:26.152 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 79/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.916e-03, size: 448, ETA: 2:59:11
2025-09-01 12:04:29.501 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 79/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.916e-03, size: 576, ETA: 2:59:08
2025-09-01 12:04:31.093 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:04:37.357 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:04:39.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:04:41.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5178
2025-09-01 12:04:41.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4300
2025-09-01 12:04:41.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2930
2025-09-01 12:04:41.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4136
2025-09-01 12:04:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:04:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:04:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.518
2025-09-01 12:04:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.430
2025-09-01 12:04:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.293
2025-09-01 12:04:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.414
2025-09-01 12:04:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:04:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:04:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:04:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:04:41.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:04:41.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:04:41.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:04:41.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:04:41.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:04:43.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:04:45.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:04:47.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:04:49.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:04:50.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:04:52.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:04:54.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:04:56.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:04:58.337 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:04:58.337 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-09-01 12:04:58.337 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-09-01 12:04:58.338 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:04:58.375 | 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

2025-09-01 12:04:58.376 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:04:58.460 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:04:58.547 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch80
2025-09-01 12:05:01.767 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 80/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.915e-03, size: 256, ETA: 2:59:04
2025-09-01 12:05:04.989 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 80/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.159s, 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: 1.915e-03, size: 256, ETA: 2:59:01
2025-09-01 12:05:08.118 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 80/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.915e-03, size: 288, ETA: 2:58:57
2025-09-01 12:05:11.237 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 80/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.155s, data_time: 0.005s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.914e-03, size: 384, ETA: 2:58:53
2025-09-01 12:05:14.565 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 80/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.164s, 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: 1.914e-03, size: 576, ETA: 2:58:51
2025-09-01 12:05:17.932 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 80/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.914e-03, size: 416, ETA: 2:58:48
2025-09-01 12:05:19.414 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:05:25.704 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:05:28.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:05:30.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5471
2025-09-01 12:05:30.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4907
2025-09-01 12:05:30.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2661
2025-09-01 12:05:30.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4346
2025-09-01 12:05:30.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:05:30.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:05:30.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.547
2025-09-01 12:05:30.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-09-01 12:05:30.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.266
2025-09-01 12:05:30.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.435
2025-09-01 12:05:30.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:05:30.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:05:30.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:05:30.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:05:30.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:05:30.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:05:30.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:05:30.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:05:30.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:05:32.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:05:35.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:05:37.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:05:40.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:05:42.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:05:44.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:05:46.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:05:49.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:05:51.390 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:05:51.390 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 12:05:51.390 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 12:05:51.390 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:05:51.418 | 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

2025-09-01 12:05:51.419 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:05:51.499 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:05:51.591 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch81
2025-09-01 12:05:54.697 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 81/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.913e-03, size: 288, ETA: 2:58:43
2025-09-01 12:05:57.805 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 81/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.153s, 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: 1.913e-03, size: 288, ETA: 2:58:39
2025-09-01 12:06:01.107 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 81/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.1, lr: 1.913e-03, size: 288, ETA: 2:58:36
2025-09-01 12:06:04.565 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 81/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.8Gb, iter_time: 0.171s, data_time: 0.006s, total_loss: 8.4, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.5, lr: 1.912e-03, size: 352, ETA: 2:58:34
2025-09-01 12:06:07.903 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 81/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.164s, 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: 1.912e-03, size: 256, ETA: 2:58:32
2025-09-01 12:06:11.186 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 81/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.162s, 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: 1.912e-03, size: 512, ETA: 2:58:29
2025-09-01 12:06:12.690 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:06:18.815 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:06:20.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:06:21.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5390
2025-09-01 12:06:22.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4725
2025-09-01 12:06:22.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2747
2025-09-01 12:06:22.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4287
2025-09-01 12:06:22.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:06:22.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:06:22.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-09-01 12:06:22.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-09-01 12:06:22.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-09-01 12:06:22.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.429
2025-09-01 12:06:22.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:06:22.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:06:22.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:06:22.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:06:22.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:06:22.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:06:22.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:06:22.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:06:22.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:06:23.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:06:25.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:06:26.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:06:28.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:06:29.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:06:31.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:06:32.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:06:34.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:06:35.536 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:06:35.537 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:06:35.537 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 12:06:35.537 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:06:35.561 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.08 ms, Average NMS time: 0.92 ms, Average inference time: 7.00 ms

2025-09-01 12:06:35.563 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:06:35.652 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:06:35.740 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch82
2025-09-01 12:06:38.846 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 82/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.154s, 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: 1.911e-03, size: 288, ETA: 2:58:23
2025-09-01 12:06:42.128 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 82/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, 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: 0.7, lr: 1.911e-03, size: 544, ETA: 2:58:20
2025-09-01 12:06:45.423 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 82/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.162s, 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: 1.910e-03, size: 576, ETA: 2:58:18
2025-09-01 12:06:48.723 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 82/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.910e-03, size: 352, ETA: 2:58:15
2025-09-01 12:06:52.018 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 82/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.163s, 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: 1.910e-03, size: 416, ETA: 2:58:12
2025-09-01 12:06:55.217 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 82/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.158s, 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: 1.909e-03, size: 256, ETA: 2:58:08
2025-09-01 12:06:56.647 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:07:03.104 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:07:06.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:07:08.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5565
2025-09-01 12:07:08.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4850
2025-09-01 12:07:08.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2990
2025-09-01 12:07:08.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4468
2025-09-01 12:07:08.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:07:08.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:07:08.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.556
2025-09-01 12:07:08.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-09-01 12:07:08.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.299
2025-09-01 12:07:08.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.447
2025-09-01 12:07:08.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:07:08.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:07:08.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:07:08.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:07:08.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:07:08.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:07:08.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:07:08.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:07:08.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:07:11.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:07:13.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:07:16.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:07:19.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:07:21.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:07:24.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:07:26.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:07:29.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:07:31.745 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:07:31.745 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:07:31.745 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:07:31.746 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:07:31.774 | 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

2025-09-01 12:07:31.775 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:07:31.855 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:07:31.941 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch83
2025-09-01 12:07:35.127 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 83/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, 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: 1.909e-03, size: 512, ETA: 2:58:03
2025-09-01 12:07:38.415 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 83/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.163s, 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: 1.909e-03, size: 544, ETA: 2:58:00
2025-09-01 12:07:41.820 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 83/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.2, cls_loss: 1.0, lr: 1.908e-03, size: 544, ETA: 2:57:58
2025-09-01 12:07:45.170 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 83/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.908e-03, size: 352, ETA: 2:57:55
2025-09-01 12:07:48.312 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 83/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.908e-03, size: 480, ETA: 2:57:52
2025-09-01 12:07:51.679 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 83/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.9, lr: 1.907e-03, size: 384, ETA: 2:57:49
2025-09-01 12:07:53.154 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:07:59.588 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:08:03.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:08:06.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5499
2025-09-01 12:08:06.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4479
2025-09-01 12:08:06.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2719
2025-09-01 12:08:06.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4232
2025-09-01 12:08:06.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:08:06.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:08:06.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.550
2025-09-01 12:08:06.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.448
2025-09-01 12:08:06.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.272
2025-09-01 12:08:06.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.423
2025-09-01 12:08:06.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:08:06.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:08:06.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:08:06.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:08:06.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:08:06.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:08:06.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:08:06.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:08:06.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:08:09.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:08:13.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:08:16.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:08:19.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:08:22.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:08:26.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:08:29.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:08:32.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:08:35.687 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:08:35.688 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 12:08:35.688 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-09-01 12:08:35.688 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:08:35.715 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.08 ms, Average NMS time: 0.98 ms, Average inference time: 7.06 ms

2025-09-01 12:08:35.716 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:08:35.848 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:08:35.936 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch84
2025-09-01 12:08:39.011 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 84/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.907e-03, size: 320, ETA: 2:57:43
2025-09-01 12:08:42.179 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 84/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.156s, 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: 1.906e-03, size: 288, ETA: 2:57:40
2025-09-01 12:08:45.324 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 84/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.906e-03, size: 416, ETA: 2:57:36
2025-09-01 12:08:48.608 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 84/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.163s, 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: 1.906e-03, size: 544, ETA: 2:57:33
2025-09-01 12:08:51.899 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 84/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.905e-03, size: 256, ETA: 2:57:30
2025-09-01 12:08:55.101 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 84/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.2, lr: 1.905e-03, size: 416, ETA: 2:57:27
2025-09-01 12:08:56.541 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:09:02.765 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:09:05.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:09:07.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5588
2025-09-01 12:09:07.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4832
2025-09-01 12:09:08.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3090
2025-09-01 12:09:08.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4503
2025-09-01 12:09:08.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:09:08.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:09:08.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-09-01 12:09:08.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.483
2025-09-01 12:09:08.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.309
2025-09-01 12:09:08.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.450
2025-09-01 12:09:08.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:09:08.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:09:08.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:09:08.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:09:08.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:09:08.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:09:08.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:09:08.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:09:08.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:09:10.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:09:12.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:09:15.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:09:17.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:09:19.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:09:22.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:09:24.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:09:26.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:09:29.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:09:29.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:09:29.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:09:29.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:09:29.401 | 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: 7.13 ms

2025-09-01 12:09:29.402 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:09:29.480 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:09:29.566 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch85
2025-09-01 12:09:32.790 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 85/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.159s, 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: 1.904e-03, size: 384, ETA: 2:57:22
2025-09-01 12:09:35.969 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 85/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, 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: 1.904e-03, size: 576, ETA: 2:57:18
2025-09-01 12:09:39.290 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 85/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.165s, 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: 1.904e-03, size: 544, ETA: 2:57:16
2025-09-01 12:09:42.566 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 85/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, 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: 1.903e-03, size: 384, ETA: 2:57:13
2025-09-01 12:09:45.981 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 85/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.168s, 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: 1.903e-03, size: 320, ETA: 2:57:10
2025-09-01 12:09:49.247 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 85/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.903e-03, size: 576, ETA: 2:57:07
2025-09-01 12:09:50.743 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:09:57.144 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:09:59.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:10:01.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5460
2025-09-01 12:10:01.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4886
2025-09-01 12:10:01.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3066
2025-09-01 12:10:01.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4471
2025-09-01 12:10:01.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:10:01.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:10:01.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.546
2025-09-01 12:10:01.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-09-01 12:10:01.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.307
2025-09-01 12:10:01.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.447
2025-09-01 12:10:01.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:10:01.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:10:01.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:10:01.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:10:01.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:10:01.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:10:01.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:10:01.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:10:01.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:10:04.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:10:06.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:10:08.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:10:10.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:10:12.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:10:14.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:10:16.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:10:18.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:10:20.790 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:10:20.790 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:10:20.790 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:10:20.790 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:10:20.816 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.33 ms, Average NMS time: 0.95 ms, Average inference time: 7.28 ms

2025-09-01 12:10:20.817 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:10:20.913 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:10:21.029 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch86
2025-09-01 12:10:24.145 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 86/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.154s, 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: 1.902e-03, size: 288, ETA: 2:57:02
2025-09-01 12:10:27.423 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 86/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.902e-03, size: 384, ETA: 2:56:59
2025-09-01 12:10:30.728 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 86/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.902e-03, size: 576, ETA: 2:56:56
2025-09-01 12:10:34.036 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 86/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, 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: 1.901e-03, size: 480, ETA: 2:56:54
2025-09-01 12:10:37.472 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 86/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.169s, 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: 1.901e-03, size: 256, ETA: 2:56:52
2025-09-01 12:10:40.642 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 86/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.155s, 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: 1.900e-03, size: 256, ETA: 2:56:48
2025-09-01 12:10:42.107 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:10:48.516 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:10:51.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:10:53.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5394
2025-09-01 12:10:53.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4939
2025-09-01 12:10:53.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3142
2025-09-01 12:10:53.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4491
2025-09-01 12:10:53.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:10:53.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:10:53.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-09-01 12:10:53.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-09-01 12:10:53.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-09-01 12:10:53.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.449
2025-09-01 12:10:53.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:10:53.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:10:53.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:10:53.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:10:53.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:10:53.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:10:53.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:10:53.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:10:53.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:10:56.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:10:58.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:11:01.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:11:03.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:11:06.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:11:08.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:11:10.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:11:13.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:11:15.587 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:11:15.587 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 12:11:15.587 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:11:15.587 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:11:15.613 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 1.00 ms, Average inference time: 7.30 ms

2025-09-01 12:11:15.614 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:11:15.757 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:11:15.853 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch87
2025-09-01 12:11:18.963 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 87/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.900e-03, size: 320, ETA: 2:56:42
2025-09-01 12:11:22.147 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 87/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.157s, 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: 1.900e-03, size: 544, ETA: 2:56:39
2025-09-01 12:11:25.326 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 87/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.899e-03, size: 544, ETA: 2:56:35
2025-09-01 12:11:28.724 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 87/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.168s, data_time: 0.006s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 1.0, lr: 1.899e-03, size: 288, ETA: 2:56:33
2025-09-01 12:11:31.925 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 87/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, 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: 1.899e-03, size: 384, ETA: 2:56:30
2025-09-01 12:11:35.208 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 87/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.898e-03, size: 288, ETA: 2:56:27
2025-09-01 12:11:36.640 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:11:43.006 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:11:46.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:11:48.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4883
2025-09-01 12:11:48.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4280
2025-09-01 12:11:48.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2751
2025-09-01 12:11:48.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3971
2025-09-01 12:11:48.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:11:48.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:11:48.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-09-01 12:11:48.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.428
2025-09-01 12:11:48.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-09-01 12:11:48.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.397
2025-09-01 12:11:48.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:11:48.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:11:48.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:11:48.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:11:48.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:11:48.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:11:48.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:11:48.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:11:48.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:11:51.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:11:54.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:11:56.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:11:59.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:12:02.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:12:04.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:12:07.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:12:10.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:12:12.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:12:12.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.15
2025-09-01 12:12:12.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.40
2025-09-01 12:12:12.645 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:12:12.672 | 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

2025-09-01 12:12:12.674 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:12:12.751 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:12:12.836 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch88
2025-09-01 12:12:15.915 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 88/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.151s, 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: 1.898e-03, size: 416, ETA: 2:56:21
2025-09-01 12:12:19.128 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 88/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.897e-03, size: 448, ETA: 2:56:17
2025-09-01 12:12:22.389 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 88/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.897e-03, size: 512, ETA: 2:56:14
2025-09-01 12:12:25.741 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 88/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.897e-03, size: 480, ETA: 2:56:11
2025-09-01 12:12:29.024 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 88/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.163s, 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: 1.896e-03, size: 576, ETA: 2:56:09
2025-09-01 12:12:32.381 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 88/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.896e-03, size: 288, ETA: 2:56:06
2025-09-01 12:12:33.826 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:12:40.298 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:12:42.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:12:44.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5497
2025-09-01 12:12:44.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4694
2025-09-01 12:12:44.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2748
2025-09-01 12:12:44.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4313
2025-09-01 12:12:44.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:12:44.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:12:44.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.550
2025-09-01 12:12:44.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-09-01 12:12:44.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-09-01 12:12:44.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.431
2025-09-01 12:12:44.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:12:44.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:12:44.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:12:44.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:12:44.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:12:44.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:12:44.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:12:44.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:12:44.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:12:46.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:12:48.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:12:49.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:12:51.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:12:53.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:12:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:12:57.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:12:59.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:13:00.936 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:13:00.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:13:00.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 12:13:00.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:13:00.962 | 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

2025-09-01 12:13:00.964 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:13:01.097 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:13:01.186 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch89
2025-09-01 12:13:04.366 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 89/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, 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: 1.895e-03, size: 384, ETA: 2:56:01
2025-09-01 12:13:07.694 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 89/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.895e-03, size: 384, ETA: 2:55:58
2025-09-01 12:13:10.959 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 89/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.162s, 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: 1.895e-03, size: 352, ETA: 2:55:55
2025-09-01 12:13:14.133 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 89/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.894e-03, size: 256, ETA: 2:55:51
2025-09-01 12:13:17.300 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 89/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.894e-03, size: 320, ETA: 2:55:48
2025-09-01 12:13:20.548 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 89/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.894e-03, size: 352, ETA: 2:55:45
2025-09-01 12:13:21.926 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:13:28.218 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:13:30.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:13:32.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5113
2025-09-01 12:13:33.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4565
2025-09-01 12:13:33.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2787
2025-09-01 12:13:33.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4155
2025-09-01 12:13:33.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:13:33.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:13:33.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-09-01 12:13:33.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.456
2025-09-01 12:13:33.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.279
2025-09-01 12:13:33.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.415
2025-09-01 12:13:33.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:13:33.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:13:33.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:13:33.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:13:33.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:13:33.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:13:33.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:13:33.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:13:33.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:13:35.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:13:37.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:13:40.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:13:42.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:13:44.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:13:46.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:13:49.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:13:51.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:13:53.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:13:53.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 12:13:53.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-09-01 12:13:53.666 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:13:53.693 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.96 ms, Average inference time: 7.28 ms

2025-09-01 12:13:53.695 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:13:53.829 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:13:53.919 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch90
2025-09-01 12:13:56.970 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 90/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.151s, 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: 1.893e-03, size: 384, ETA: 2:55:38
2025-09-01 12:14:00.227 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 90/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.893e-03, size: 576, ETA: 2:55:35
2025-09-01 12:14:03.701 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 90/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.171s, 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: 1.892e-03, size: 480, ETA: 2:55:33
2025-09-01 12:14:07.037 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 90/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.892e-03, size: 320, ETA: 2:55:31
2025-09-01 12:14:10.330 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 90/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.162s, 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: 1.892e-03, size: 416, ETA: 2:55:28
2025-09-01 12:14:13.610 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 90/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.891e-03, size: 512, ETA: 2:55:25
2025-09-01 12:14:15.135 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:14:21.341 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:14:23.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:14:24.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5736
2025-09-01 12:14:25.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4984
2025-09-01 12:14:25.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2843
2025-09-01 12:14:25.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4521
2025-09-01 12:14:25.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:14:25.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:14:25.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-09-01 12:14:25.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-09-01 12:14:25.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.284
2025-09-01 12:14:25.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.452
2025-09-01 12:14:25.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:14:25.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:14:25.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:14:25.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:14:25.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:14:25.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:14:25.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:14:25.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:14:25.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:14:26.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:14:28.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:14:30.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:14:31.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:14:33.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:14:35.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:14:36.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:14:38.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:14:40.254 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:14:40.254 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 12:14:40.254 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:14:40.254 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:14:40.279 | 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

2025-09-01 12:14:40.281 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:14:40.368 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:14:40.458 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch91
2025-09-01 12:14:43.686 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 91/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, 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: 1.891e-03, size: 480, ETA: 2:55:21
2025-09-01 12:14:46.938 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 91/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.160s, 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: 1.890e-03, size: 416, ETA: 2:55:17
2025-09-01 12:14:50.075 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 91/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.890e-03, size: 352, ETA: 2:55:14
2025-09-01 12:14:53.362 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 91/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.1, lr: 1.890e-03, size: 480, ETA: 2:55:11
2025-09-01 12:14:56.658 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 91/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.889e-03, size: 480, ETA: 2:55:08
2025-09-01 12:14:59.902 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 91/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.889e-03, size: 288, ETA: 2:55:05
2025-09-01 12:15:01.484 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:15:07.822 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:15:09.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:15:11.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5300
2025-09-01 12:15:11.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4687
2025-09-01 12:15:11.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2788
2025-09-01 12:15:11.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4258
2025-09-01 12:15:11.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:15:11.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:15:11.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-09-01 12:15:11.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-09-01 12:15:11.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.279
2025-09-01 12:15:11.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.426
2025-09-01 12:15:11.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:15:11.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:15:11.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:15:11.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:15:11.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:15:11.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:15:11.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:15:11.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:15:11.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:15:13.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:15:15.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:15:16.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:15:18.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:15:20.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:15:21.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:15:23.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:15:25.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:15:26.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:15:26.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:15:26.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 12:15:26.862 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:15:26.889 | 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

2025-09-01 12:15:26.890 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:15:26.976 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:15:27.065 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch92
2025-09-01 12:15:30.218 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 92/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.888e-03, size: 448, ETA: 2:55:00
2025-09-01 12:15:33.434 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 92/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.158s, 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: 1.888e-03, size: 320, ETA: 2:54:57
2025-09-01 12:15:36.726 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 92/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, 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: 0.8, lr: 1.887e-03, size: 480, ETA: 2:54:54
2025-09-01 12:15:40.071 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 92/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.887e-03, size: 416, ETA: 2:54:51
2025-09-01 12:15:43.335 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 92/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.162s, 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: 1.887e-03, size: 544, ETA: 2:54:48
2025-09-01 12:15:46.736 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 92/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.886e-03, size: 512, ETA: 2:54:46
2025-09-01 12:15:48.312 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:15:54.464 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:15:56.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:15:57.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5450
2025-09-01 12:15:57.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5137
2025-09-01 12:15:57.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2709
2025-09-01 12:15:57.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4432
2025-09-01 12:15:57.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:15:57.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:15:57.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-09-01 12:15:57.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.514
2025-09-01 12:15:57.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.271
2025-09-01 12:15:57.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.443
2025-09-01 12:15:57.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:15:57.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:15:57.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:15:57.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:15:57.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:15:57.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:15:57.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:15:57.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:15:57.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:15:59.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:16:00.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:16:02.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:16:03.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:16:04.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:16:06.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:16:07.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:16:09.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:16:10.556 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:16:10.556 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:16:10.556 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 12:16:10.556 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:16:10.566 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.01 ms, Average NMS time: 0.97 ms, Average inference time: 6.98 ms

2025-09-01 12:16:10.568 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:16:10.655 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:16:10.748 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch93
2025-09-01 12:16:13.984 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 93/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.886e-03, size: 480, ETA: 2:54:42
2025-09-01 12:16:17.308 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 93/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.164s, 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: 1.885e-03, size: 544, ETA: 2:54:39
2025-09-01 12:16:20.596 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 93/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.885e-03, size: 352, ETA: 2:54:36
2025-09-01 12:16:23.907 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 93/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.885e-03, size: 512, ETA: 2:54:33
2025-09-01 12:16:27.131 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 93/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.884e-03, size: 256, ETA: 2:54:30
2025-09-01 12:16:30.394 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 93/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.884e-03, size: 256, ETA: 2:54:27
2025-09-01 12:16:31.867 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:16:38.184 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:16:40.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:16:43.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5355
2025-09-01 12:16:43.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4465
2025-09-01 12:16:43.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3380
2025-09-01 12:16:43.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4400
2025-09-01 12:16:43.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:16:43.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:16:43.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-09-01 12:16:43.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.447
2025-09-01 12:16:43.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.338
2025-09-01 12:16:43.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.440
2025-09-01 12:16:43.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:16:43.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:16:43.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:16:43.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:16:43.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:16:43.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:16:43.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:16:43.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:16:43.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:16:45.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:16:47.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:16:50.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:16:52.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:16:54.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:16:57.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:16:59.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:17:01.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:17:04.253 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:17:04.253 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 12:17:04.253 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 12:17:04.253 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:17:04.281 | 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

2025-09-01 12:17:04.282 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:17:04.362 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:17:04.446 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch94
2025-09-01 12:17:07.508 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 94/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.883e-03, size: 384, ETA: 2:54:21
2025-09-01 12:17:10.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 94/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.155s, 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: 1.883e-03, size: 384, ETA: 2:54:18
2025-09-01 12:17:13.898 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 94/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.160s, 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: 1.883e-03, size: 384, ETA: 2:54:14
2025-09-01 12:17:17.217 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 94/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.882e-03, size: 512, ETA: 2:54:12
2025-09-01 12:17:20.405 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 94/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.159s, 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: 1.882e-03, size: 544, ETA: 2:54:08
2025-09-01 12:17:23.665 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 94/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.881e-03, size: 512, ETA: 2:54:05
2025-09-01 12:17:25.093 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:17:31.378 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:17:33.660 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:17:35.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5695
2025-09-01 12:17:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4960
2025-09-01 12:17:35.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2944
2025-09-01 12:17:35.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4533
2025-09-01 12:17:35.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:17:35.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:17:35.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.570
2025-09-01 12:17:35.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-09-01 12:17:35.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.294
2025-09-01 12:17:35.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.453
2025-09-01 12:17:35.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:17:35.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:17:35.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:17:35.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:17:35.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:17:35.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:17:35.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:17:35.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:17:35.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:17:37.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:17:39.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:17:41.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:17:43.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:17:45.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:17:47.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:17:49.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:17:51.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:17:53.000 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:17:53.000 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:17:53.000 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:17:53.001 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:17:53.027 | 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

2025-09-01 12:17:53.028 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:17:53.143 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:17:53.275 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch95
2025-09-01 12:17:56.407 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 95/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.156s, 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: 1.881e-03, size: 576, ETA: 2:54:00
2025-09-01 12:17:59.858 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 95/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.170s, 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: 1.881e-03, size: 384, ETA: 2:53:58
2025-09-01 12:18:03.050 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 95/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.880e-03, size: 512, ETA: 2:53:54
2025-09-01 12:18:06.325 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 95/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.880e-03, size: 320, ETA: 2:53:51
2025-09-01 12:18:09.562 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 95/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.879e-03, size: 416, ETA: 2:53:48
2025-09-01 12:18:12.743 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 95/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.158s, 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: 1.879e-03, size: 544, ETA: 2:53:45
2025-09-01 12:18:14.220 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:18:20.448 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:18:23.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:18:25.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5476
2025-09-01 12:18:26.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4832
2025-09-01 12:18:26.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2709
2025-09-01 12:18:26.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4339
2025-09-01 12:18:26.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:18:26.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:18:26.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.548
2025-09-01 12:18:26.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.483
2025-09-01 12:18:26.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.271
2025-09-01 12:18:26.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-09-01 12:18:26.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:18:26.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:18:26.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:18:26.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:18:26.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:18:26.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:18:26.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:18:26.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:18:26.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:18:28.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:18:31.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:18:33.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:18:36.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:18:39.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:18:41.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:18:44.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:18:46.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:18:49.437 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:18:49.438 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 12:18:49.438 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 12:18:49.438 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:18:49.472 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 5.96 ms, Average NMS time: 0.96 ms, Average inference time: 6.92 ms

2025-09-01 12:18:49.473 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:18:49.552 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:18:49.642 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch96
2025-09-01 12:18:52.786 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 96/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.155s, 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: 1.878e-03, size: 288, ETA: 2:53:40
2025-09-01 12:18:56.172 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 96/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.6, lr: 1.878e-03, size: 480, ETA: 2:53:37
2025-09-01 12:18:59.588 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 96/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.7, lr: 1.878e-03, size: 256, ETA: 2:53:35
2025-09-01 12:19:02.913 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 96/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.877e-03, size: 288, ETA: 2:53:32
2025-09-01 12:19:06.059 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 96/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.877e-03, size: 288, ETA: 2:53:28
2025-09-01 12:19:09.280 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 96/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, 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: 0.9, lr: 1.876e-03, size: 256, ETA: 2:53:25
2025-09-01 12:19:10.786 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:19:17.011 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:19:19.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:19:21.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5544
2025-09-01 12:19:21.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4950
2025-09-01 12:19:21.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3225
2025-09-01 12:19:21.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4573
2025-09-01 12:19:21.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:19:21.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:19:21.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.554
2025-09-01 12:19:21.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-09-01 12:19:21.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.322
2025-09-01 12:19:21.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.457
2025-09-01 12:19:21.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:19:21.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:19:21.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:19:21.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:19:21.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:19:21.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:19:21.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:19:21.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:19:21.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:19:23.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:19:25.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:19:27.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:19:29.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:19:31.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:19:33.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:19:36.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:19:38.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:19:40.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:19:40.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 12:19:40.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 12:19:40.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:19:40.153 | 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

2025-09-01 12:19:40.154 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:19:40.284 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:19:40.369 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch97
2025-09-01 12:19:43.470 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 97/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.876e-03, size: 480, ETA: 2:53:20
2025-09-01 12:19:46.706 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 97/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.159s, 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: 1.876e-03, size: 352, ETA: 2:53:17
2025-09-01 12:19:50.027 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 97/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.164s, 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: 1.875e-03, size: 416, ETA: 2:53:14
2025-09-01 12:19:53.306 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 97/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.875e-03, size: 512, ETA: 2:53:11
2025-09-01 12:19:56.598 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 97/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, 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: 1.874e-03, size: 256, ETA: 2:53:08
2025-09-01 12:19:59.853 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 97/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.2, lr: 1.874e-03, size: 320, ETA: 2:53:05
2025-09-01 12:20:01.276 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:20:07.702 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:20:09.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:20:10.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5430
2025-09-01 12:20:10.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4649
2025-09-01 12:20:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2878
2025-09-01 12:20:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4319
2025-09-01 12:20:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:20:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:20:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-09-01 12:20:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.465
2025-09-01 12:20:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.288
2025-09-01 12:20:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.432
2025-09-01 12:20:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:20:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:20:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:20:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:20:11.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:20:11.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:20:11.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:20:11.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:20:11.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:20:12.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:20:14.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:20:15.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:20:17.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:20:18.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:20:20.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:20:21.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:20:23.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:20:24.818 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:20:24.819 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:20:24.819 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 12:20:24.819 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:20:24.844 | 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

2025-09-01 12:20:24.845 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:20:24.924 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:20:25.010 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch98
2025-09-01 12:20:28.202 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 98/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.873e-03, size: 448, ETA: 2:53:00
2025-09-01 12:20:31.481 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 98/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, 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: 1.873e-03, size: 512, ETA: 2:52:57
2025-09-01 12:20:34.797 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 98/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.873e-03, size: 576, ETA: 2:52:54
2025-09-01 12:20:38.203 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 98/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.167s, 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: 1.872e-03, size: 352, ETA: 2:52:52
2025-09-01 12:20:41.324 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 98/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.872e-03, size: 256, ETA: 2:52:48
2025-09-01 12:20:44.553 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 98/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, 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: 1.871e-03, size: 320, ETA: 2:52:45
2025-09-01 12:20:46.091 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:20:52.289 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:20:54.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:20:56.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5577
2025-09-01 12:20:56.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4772
2025-09-01 12:20:56.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2625
2025-09-01 12:20:56.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4325
2025-09-01 12:20:56.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:20:56.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:20:56.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.558
2025-09-01 12:20:56.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.477
2025-09-01 12:20:56.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.263
2025-09-01 12:20:56.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.432
2025-09-01 12:20:56.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:20:56.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:20:56.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:20:56.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:20:56.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:20:56.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:20:56.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:20:56.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:20:56.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:20:59.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:21:01.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:21:03.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:21:05.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:21:07.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:21:09.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:21:11.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:21:13.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:21:16.159 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:21:16.159 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:21:16.159 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 12:21:16.159 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:21:16.185 | 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: 7.27 ms

2025-09-01 12:21:16.186 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:21:16.274 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:21:16.362 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch99
2025-09-01 12:21:19.641 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 99/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.3, lr: 1.871e-03, size: 384, ETA: 2:52:41
2025-09-01 12:21:22.892 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 99/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.870e-03, size: 288, ETA: 2:52:37
2025-09-01 12:21:26.184 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 99/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.0, lr: 1.870e-03, size: 544, ETA: 2:52:34
2025-09-01 12:21:29.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 99/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.870e-03, size: 352, ETA: 2:52:31
2025-09-01 12:21:32.664 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 99/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, 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: 1.869e-03, size: 320, ETA: 2:52:28
2025-09-01 12:21:35.842 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 99/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.157s, 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: 1.869e-03, size: 576, ETA: 2:52:24
2025-09-01 12:21:37.437 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:21:43.637 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:21:45.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:21:46.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5206
2025-09-01 12:21:46.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4738
2025-09-01 12:21:46.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2641
2025-09-01 12:21:46.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4195
2025-09-01 12:21:46.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:21:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:21:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 12:21:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.474
2025-09-01 12:21:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.264
2025-09-01 12:21:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.419
2025-09-01 12:21:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:21:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:21:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:21:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:21:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:21:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:21:46.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:21:46.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:21:46.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:21:48.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:21:49.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:21:51.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:21:52.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:21:53.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:21:55.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:21:56.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:21:58.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:21:59.412 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:21:59.413 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 12:21:59.413 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-09-01 12:21:59.413 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:21:59.423 | 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: 7.21 ms

2025-09-01 12:21:59.426 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:21:59.555 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:21:59.644 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch100
2025-09-01 12:22:02.859 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 100/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.868e-03, size: 576, ETA: 2:52:20
2025-09-01 12:22:06.231 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 100/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.2, lr: 1.868e-03, size: 512, ETA: 2:52:18
2025-09-01 12:22:09.653 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 100/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.169s, 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: 1.867e-03, size: 256, ETA: 2:52:15
2025-09-01 12:22:12.919 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 100/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.8, lr: 1.867e-03, size: 288, ETA: 2:52:12
2025-09-01 12:22:16.229 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 100/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.6, lr: 1.867e-03, size: 384, ETA: 2:52:09
2025-09-01 12:22:19.367 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 100/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.154s, 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: 1.866e-03, size: 320, ETA: 2:52:06
2025-09-01 12:22:20.811 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:22:27.030 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:22:29.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:22:32.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5442
2025-09-01 12:22:32.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4943
2025-09-01 12:22:32.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2782
2025-09-01 12:22:32.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4389
2025-09-01 12:22:32.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:22:32.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:22:32.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.544
2025-09-01 12:22:32.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-09-01 12:22:32.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.278
2025-09-01 12:22:32.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.439
2025-09-01 12:22:32.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:22:32.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:22:32.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:22:32.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:22:32.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:22:32.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:22:32.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:22:32.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:22:32.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:22:34.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:22:37.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:22:39.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:22:42.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:22:44.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:22:47.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:22:49.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:22:52.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:22:54.421 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:22:54.421 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:22:54.421 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 12:22:54.421 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:22:54.447 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.10 ms, Average NMS time: 0.92 ms, Average inference time: 7.03 ms

2025-09-01 12:22:54.449 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:22:54.567 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:22:54.708 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch101
2025-09-01 12:22:57.720 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 101/600, iter: 20/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.866e-03, size: 384, ETA: 2:52:00
2025-09-01 12:23:01.035 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 101/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, 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: 1.865e-03, size: 576, ETA: 2:51:57
2025-09-01 12:23:04.249 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 101/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.160s, 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: 1.865e-03, size: 416, ETA: 2:51:54
2025-09-01 12:23:07.642 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 101/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.168s, data_time: 0.003s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.864e-03, size: 288, ETA: 2:51:51
2025-09-01 12:23:10.910 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 101/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.162s, 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: 1.864e-03, size: 480, ETA: 2:51:48
2025-09-01 12:23:14.180 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 101/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, 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: 1.864e-03, size: 576, ETA: 2:51:45
2025-09-01 12:23:15.583 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:23:21.859 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:23:24.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:23:26.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5269
2025-09-01 12:23:27.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4787
2025-09-01 12:23:27.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2935
2025-09-01 12:23:27.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4331
2025-09-01 12:23:27.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:23:27.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:23:27.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-09-01 12:23:27.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-09-01 12:23:27.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.294
2025-09-01 12:23:27.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.433
2025-09-01 12:23:27.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:23:27.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:23:27.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:23:27.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:23:27.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:23:27.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:23:27.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:23:27.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:23:27.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:23:29.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:23:32.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:23:34.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:23:36.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:23:39.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:23:41.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:23:44.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:23:46.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:23:48.920 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:23:48.920 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 12:23:48.920 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 12:23:48.920 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:23:48.946 | 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

2025-09-01 12:23:48.947 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:23:49.071 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:23:49.157 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch102
2025-09-01 12:23:52.368 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 102/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.863e-03, size: 544, ETA: 2:51:40
2025-09-01 12:23:55.551 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 102/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.863e-03, size: 352, ETA: 2:51:37
2025-09-01 12:23:58.730 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 102/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.157s, 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: 1.862e-03, size: 384, ETA: 2:51:33
2025-09-01 12:24:02.086 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 102/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.862e-03, size: 384, ETA: 2:51:31
2025-09-01 12:24:05.296 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 102/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.861e-03, size: 576, ETA: 2:51:27
2025-09-01 12:24:08.663 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 102/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.861e-03, size: 576, ETA: 2:51:25
2025-09-01 12:24:10.141 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:24:16.422 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:24:19.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:24:21.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5722
2025-09-01 12:24:21.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5126
2025-09-01 12:24:22.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2692
2025-09-01 12:24:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4513
2025-09-01 12:24:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:24:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:24:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-09-01 12:24:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-09-01 12:24:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.269
2025-09-01 12:24:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.451
2025-09-01 12:24:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:24:22.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:24:22.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:24:22.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:24:22.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:24:22.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:24:22.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:24:22.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:24:22.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:24:24.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:24:27.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:24:29.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:24:32.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:24:35.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:24:37.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:24:40.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:24:42.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:24:45.559 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:24:45.560 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:24:45.560 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:24:45.560 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:24:45.585 | 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

2025-09-01 12:24:45.587 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:24:45.668 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:24:45.753 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch103
2025-09-01 12:24:49.013 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 103/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.860e-03, size: 448, ETA: 2:51:20
2025-09-01 12:24:52.180 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 103/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.157s, 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: 1.860e-03, size: 448, ETA: 2:51:17
2025-09-01 12:24:55.475 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 103/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, 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: 1.859e-03, size: 448, ETA: 2:51:14
2025-09-01 12:24:58.678 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 103/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.859e-03, size: 544, ETA: 2:51:10
2025-09-01 12:25:01.875 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 103/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.157s, 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: 1.859e-03, size: 448, ETA: 2:51:07
2025-09-01 12:25:05.117 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 103/600, iter: 120/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.858e-03, size: 384, ETA: 2:51:04
2025-09-01 12:25:06.526 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:25:12.794 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:25:14.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:25:16.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5519
2025-09-01 12:25:16.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4507
2025-09-01 12:25:16.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2469
2025-09-01 12:25:16.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4165
2025-09-01 12:25:16.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:25:16.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:25:16.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.552
2025-09-01 12:25:16.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.451
2025-09-01 12:25:16.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.247
2025-09-01 12:25:16.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.416
2025-09-01 12:25:16.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:25:16.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:25:16.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:25:16.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:25:16.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:25:16.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:25:16.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:25:16.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:25:16.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:25:18.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:25:19.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:25:21.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:25:23.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:25:24.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:25:26.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:25:28.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:25:29.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:25:31.347 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:25:31.348 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 12:25:31.348 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-09-01 12:25:31.348 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:25:31.372 | 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

2025-09-01 12:25:31.378 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:25:31.513 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:25:31.598 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch104
2025-09-01 12:25:34.870 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 104/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.161s, 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: 1.858e-03, size: 416, ETA: 2:50:59
2025-09-01 12:25:38.151 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 104/600, iter: 40/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.161s, 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: 1.857e-03, size: 512, ETA: 2:50:56
2025-09-01 12:25:41.480 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 104/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 4.2, cls_loss: 1.2, lr: 1.857e-03, size: 512, ETA: 2:50:53
2025-09-01 12:25:44.698 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 104/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.856e-03, size: 384, ETA: 2:50:50
2025-09-01 12:25:47.945 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 104/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.160s, 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: 1.856e-03, size: 512, ETA: 2:50:46
2025-09-01 12:25:51.143 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 104/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.856e-03, size: 256, ETA: 2:50:43
2025-09-01 12:25:52.561 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:25:58.978 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:26:03.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:26:06.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5561
2025-09-01 12:26:06.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4793
2025-09-01 12:26:06.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3206
2025-09-01 12:26:06.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4520
2025-09-01 12:26:06.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:26:06.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:26:06.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.556
2025-09-01 12:26:06.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-09-01 12:26:06.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.321
2025-09-01 12:26:06.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.452
2025-09-01 12:26:06.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:26:06.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:26:06.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:26:06.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:26:06.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:26:06.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:26:06.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:26:06.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:26:06.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:26:10.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:26:13.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:26:17.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:26:20.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:26:24.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:26:27.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:26:31.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:26:34.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:26:38.323 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:26:38.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:26:38.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:26:38.324 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:26:38.353 | 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

2025-09-01 12:26:38.354 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:26:38.433 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:26:38.520 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch105
2025-09-01 12:26:41.516 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 105/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.1, lr: 1.855e-03, size: 512, ETA: 2:50:37
2025-09-01 12:26:44.729 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 105/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.3, lr: 1.855e-03, size: 320, ETA: 2:50:34
2025-09-01 12:26:47.994 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 105/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.161s, 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: 1.854e-03, size: 576, ETA: 2:50:31
2025-09-01 12:26:51.238 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 105/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.854e-03, size: 320, ETA: 2:50:27
2025-09-01 12:26:54.522 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 105/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.853e-03, size: 544, ETA: 2:50:24
2025-09-01 12:26:57.783 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 105/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, 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: 0.9, lr: 1.853e-03, size: 480, ETA: 2:50:21
2025-09-01 12:26:59.206 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:27:05.508 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:27:07.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:27:09.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5578
2025-09-01 12:27:09.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4894
2025-09-01 12:27:09.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3176
2025-09-01 12:27:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4549
2025-09-01 12:27:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:27:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:27:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.558
2025-09-01 12:27:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-09-01 12:27:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.318
2025-09-01 12:27:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.455
2025-09-01 12:27:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:27:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:27:09.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:27:09.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:27:09.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:27:09.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:27:09.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:27:09.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:27:09.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:27:11.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:27:13.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:27:15.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:27:16.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:27:18.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:27:20.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:27:22.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:27:24.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:27:25.856 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:27:25.856 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:27:25.856 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:27:25.856 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:27:25.882 | 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

2025-09-01 12:27:25.884 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:27:26.052 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:27:26.148 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch106
2025-09-01 12:27:29.361 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 106/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.0, lr: 1.852e-03, size: 320, ETA: 2:50:16
2025-09-01 12:27:32.685 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 106/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.852e-03, size: 544, ETA: 2:50:13
2025-09-01 12:27:36.067 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 106/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.167s, 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: 1.851e-03, size: 576, ETA: 2:50:11
2025-09-01 12:27:39.436 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 106/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 7.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 1.851e-03, size: 320, ETA: 2:50:08
2025-09-01 12:27:42.655 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 106/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.158s, 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: 1.851e-03, size: 352, ETA: 2:50:05
2025-09-01 12:27:45.840 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 106/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.6, lr: 1.850e-03, size: 320, ETA: 2:50:01
2025-09-01 12:27:47.253 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:27:53.621 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:27:56.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:27:58.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5498
2025-09-01 12:27:58.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4856
2025-09-01 12:27:58.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2896
2025-09-01 12:27:58.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4417
2025-09-01 12:27:58.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:27:58.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:27:58.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.550
2025-09-01 12:27:58.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-09-01 12:27:58.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-09-01 12:27:58.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.442
2025-09-01 12:27:58.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:27:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:27:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:27:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:27:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:27:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:27:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:27:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:27:58.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:28:01.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:28:03.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:28:06.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:28:08.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:28:11.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:28:13.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:28:16.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:28:18.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:28:21.017 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:28:21.017 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:28:21.017 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 12:28:21.017 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:28:21.047 | 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

2025-09-01 12:28:21.050 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:28:21.134 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:28:21.225 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch107
2025-09-01 12:28:24.398 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 107/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.849e-03, size: 352, ETA: 2:49:56
2025-09-01 12:28:27.536 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 107/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.849e-03, size: 288, ETA: 2:49:53
2025-09-01 12:28:30.822 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 107/600, iter: 60/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.849e-03, size: 384, ETA: 2:49:50
2025-09-01 12:28:34.116 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 107/600, iter: 80/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.848e-03, size: 544, ETA: 2:49:47
2025-09-01 12:28:37.417 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 107/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.848e-03, size: 416, ETA: 2:49:44
2025-09-01 12:28:40.831 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 107/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.168s, 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: 1.847e-03, size: 384, ETA: 2:49:41
2025-09-01 12:28:42.271 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:28:48.630 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:28:51.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:28:54.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5025
2025-09-01 12:28:54.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4863
2025-09-01 12:28:54.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3124
2025-09-01 12:28:54.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4337
2025-09-01 12:28:54.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:28:54.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:28:54.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-09-01 12:28:54.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-09-01 12:28:54.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-09-01 12:28:54.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-09-01 12:28:54.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:28:54.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:28:54.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:28:54.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:28:54.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:28:54.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:28:54.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:28:54.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:28:54.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:28:57.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:29:00.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:29:03.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:29:06.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:29:09.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:29:11.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:29:14.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:29:17.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:29:20.269 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:29:20.270 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-09-01 12:29:20.270 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 12:29:20.270 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:29:20.296 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.98 ms, Average inference time: 7.30 ms

2025-09-01 12:29:20.297 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:29:20.379 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:29:20.467 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch108
2025-09-01 12:29:23.787 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 108/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, 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: 1.847e-03, size: 256, ETA: 2:49:37
2025-09-01 12:29:26.914 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 108/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.155s, 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: 1.846e-03, size: 288, ETA: 2:49:33
2025-09-01 12:29:30.119 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 108/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 7.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.846e-03, size: 288, ETA: 2:49:30
2025-09-01 12:29:33.341 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 108/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.158s, 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: 1.845e-03, size: 288, ETA: 2:49:26
2025-09-01 12:29:36.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 108/600, iter: 100/129, gpu mem: 1330Mb, mem: 45.9Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.845e-03, size: 512, ETA: 2:49:23
2025-09-01 12:29:39.720 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 108/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.160s, 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: 1.845e-03, size: 320, ETA: 2:49:19
2025-09-01 12:29:41.195 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:29:47.512 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:29:50.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:29:53.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5679
2025-09-01 12:29:53.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5102
2025-09-01 12:29:53.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2882
2025-09-01 12:29:53.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4554
2025-09-01 12:29:53.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:29:53.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:29:53.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.568
2025-09-01 12:29:53.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.510
2025-09-01 12:29:53.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.288
2025-09-01 12:29:53.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.455
2025-09-01 12:29:53.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:29:53.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:29:53.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:29:53.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:29:53.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:29:53.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:29:53.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:29:53.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:29:53.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:29:56.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:29:59.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:30:01.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:30:04.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:30:07.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:30:10.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:30:12.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:30:15.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:30:18.113 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:30:18.113 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 12:30:18.114 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 12:30:18.114 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:30:18.139 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.96 ms, Average inference time: 7.25 ms

2025-09-01 12:30:18.140 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:30:18.217 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:30:18.302 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch109
2025-09-01 12:30:21.510 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 109/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.844e-03, size: 512, ETA: 2:49:15
2025-09-01 12:30:24.738 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 109/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.1, lr: 1.843e-03, size: 320, ETA: 2:49:11
2025-09-01 12:30:27.888 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 109/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.843e-03, size: 576, ETA: 2:49:08
2025-09-01 12:30:31.272 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 109/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 1.8, cls_loss: 0.6, lr: 1.843e-03, size: 448, ETA: 2:49:05
2025-09-01 12:30:34.544 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 109/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.842e-03, size: 480, ETA: 2:49:02
2025-09-01 12:30:37.824 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 109/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, 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: 1.842e-03, size: 320, ETA: 2:48:59
2025-09-01 12:30:39.283 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:30:45.577 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:30:48.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:30:51.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5166
2025-09-01 12:30:51.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4817
2025-09-01 12:30:51.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2452
2025-09-01 12:30:51.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4145
2025-09-01 12:30:51.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:30:51.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:30:51.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 12:30:51.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-09-01 12:30:51.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.245
2025-09-01 12:30:51.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.415
2025-09-01 12:30:51.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:30:51.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:30:51.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:30:51.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:30:51.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:30:51.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:30:51.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:30:51.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:30:51.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:30:54.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:30:57.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:30:59.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:31:02.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:31:05.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:31:08.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:31:11.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:31:13.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:31:16.617 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:31:16.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:31:16.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-09-01 12:31:16.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:31:16.644 | 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: 7.27 ms

2025-09-01 12:31:16.651 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:31:16.772 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:31:16.874 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch110
2025-09-01 12:31:20.033 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 110/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.841e-03, size: 288, ETA: 2:48:54
2025-09-01 12:31:23.279 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 110/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.841e-03, size: 544, ETA: 2:48:51
2025-09-01 12:31:26.553 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 110/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 8.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.2, lr: 1.840e-03, size: 256, ETA: 2:48:48
2025-09-01 12:31:29.736 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 110/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.840e-03, size: 320, ETA: 2:48:44
2025-09-01 12:31:33.034 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 110/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.162s, 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: 1.839e-03, size: 352, ETA: 2:48:41
2025-09-01 12:31:36.255 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 110/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.7, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.8, lr: 1.839e-03, size: 288, ETA: 2:48:38
2025-09-01 12:31:37.669 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:31:43.987 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:31:47.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:31:50.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5787
2025-09-01 12:31:50.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4894
2025-09-01 12:31:50.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3152
2025-09-01 12:31:50.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4611
2025-09-01 12:31:50.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:31:50.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:31:50.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-09-01 12:31:50.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-09-01 12:31:50.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.315
2025-09-01 12:31:50.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.461
2025-09-01 12:31:50.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:31:50.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:31:50.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:31:50.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:31:50.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:31:50.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:31:50.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:31:50.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:31:50.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:31:53.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:31:56.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:31:59.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:32:02.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:32:05.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:32:08.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:32:11.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:32:14.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:32:16.918 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:32:16.918 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 12:32:16.918 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 12:32:16.918 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:32:16.947 | 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

2025-09-01 12:32:16.948 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:32:17.030 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:32:17.118 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch111
2025-09-01 12:32:20.153 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 111/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.151s, 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: 1.838e-03, size: 384, ETA: 2:48:32
2025-09-01 12:32:23.449 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 111/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, 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: 1.838e-03, size: 352, ETA: 2:48:29
2025-09-01 12:32:26.714 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 111/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.160s, data_time: 0.003s, total_loss: 5.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.837e-03, size: 384, ETA: 2:48:26
2025-09-01 12:32:30.030 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 111/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, 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: 1.837e-03, size: 448, ETA: 2:48:23
2025-09-01 12:32:33.326 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 111/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.836e-03, size: 512, ETA: 2:48:20
2025-09-01 12:32:36.533 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 111/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.836e-03, size: 416, ETA: 2:48:17
2025-09-01 12:32:38.056 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:32:44.329 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:32:45.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:32:46.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5422
2025-09-01 12:32:46.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4152
2025-09-01 12:32:47.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2661
2025-09-01 12:32:47.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4078
2025-09-01 12:32:47.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:32:47.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:32:47.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-09-01 12:32:47.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-09-01 12:32:47.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.266
2025-09-01 12:32:47.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.408
2025-09-01 12:32:47.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:32:47.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:32:47.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:32:47.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:32:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:32:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:32:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:32:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:32:47.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:32:48.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:32:49.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:32:50.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:32:51.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:32:53.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:32:54.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:32:55.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:32:56.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:32:57.856 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:32:57.856 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-09-01 12:32:57.857 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-09-01 12:32:57.857 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:32:57.865 | 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

2025-09-01 12:32:57.866 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:32:57.941 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:32:58.027 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch112
2025-09-01 12:33:01.134 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 112/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.154s, 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: 1.835e-03, size: 256, ETA: 2:48:12
2025-09-01 12:33:04.638 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 112/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.173s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.835e-03, size: 480, ETA: 2:48:10
2025-09-01 12:33:07.831 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 112/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.158s, data_time: 0.006s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.9, lr: 1.834e-03, size: 256, ETA: 2:48:06
2025-09-01 12:33:11.064 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 112/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, 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: 1.834e-03, size: 480, ETA: 2:48:03
2025-09-01 12:33:14.298 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 112/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.834e-03, size: 512, ETA: 2:48:00
2025-09-01 12:33:17.652 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 112/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.833e-03, size: 448, ETA: 2:47:57
2025-09-01 12:33:19.106 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:33:25.386 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:33:27.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:33:29.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5571
2025-09-01 12:33:30.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4835
2025-09-01 12:33:30.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2872
2025-09-01 12:33:30.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4426
2025-09-01 12:33:30.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:33:30.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:33:30.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.557
2025-09-01 12:33:30.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.483
2025-09-01 12:33:30.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.287
2025-09-01 12:33:30.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.443
2025-09-01 12:33:30.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:33:30.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:33:30.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:33:30.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:33:30.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:33:30.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:33:30.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:33:30.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:33:30.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:33:32.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:33:34.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:33:36.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:33:38.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:33:40.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:33:42.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:33:44.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:33:46.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:33:49.030 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:33:49.030 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:33:49.030 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 12:33:49.030 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:33:49.059 | 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

2025-09-01 12:33:49.061 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:33:49.145 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:33:49.242 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch113
2025-09-01 12:33:52.471 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 113/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, 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: 1.832e-03, size: 448, ETA: 2:47:53
2025-09-01 12:33:55.744 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 113/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.161s, 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: 1.832e-03, size: 512, ETA: 2:47:49
2025-09-01 12:33:59.128 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 113/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.832e-03, size: 416, ETA: 2:47:47
2025-09-01 12:34:02.410 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 113/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.831e-03, size: 544, ETA: 2:47:44
2025-09-01 12:34:05.728 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 113/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, 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: 1.831e-03, size: 320, ETA: 2:47:41
2025-09-01 12:34:09.113 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 113/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.166s, 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: 1.830e-03, size: 448, ETA: 2:47:38
2025-09-01 12:34:10.597 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:34:16.966 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:34:22.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:34:25.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5205
2025-09-01 12:34:26.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4712
2025-09-01 12:34:26.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3104
2025-09-01 12:34:26.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4340
2025-09-01 12:34:26.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:34:26.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:34:26.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-09-01 12:34:26.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-09-01 12:34:26.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.310
2025-09-01 12:34:26.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-09-01 12:34:26.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:34:26.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:34:26.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:34:26.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:34:26.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:34:26.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:34:26.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:34:26.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:34:26.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:34:30.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:34:34.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:34:38.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:34:41.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:34:45.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:34:49.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:34:53.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:34:57.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:35:01.533 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:35:01.533 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:35:01.533 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 12:35:01.533 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:35:01.563 | 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

2025-09-01 12:35:01.564 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:35:01.695 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:35:01.781 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch114
2025-09-01 12:35:05.083 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 114/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.830e-03, size: 384, ETA: 2:47:34
2025-09-01 12:35:08.327 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 114/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, 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: 1.829e-03, size: 416, ETA: 2:47:30
2025-09-01 12:35:11.478 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 114/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.829e-03, size: 384, ETA: 2:47:27
2025-09-01 12:35:14.738 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 114/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.161s, 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: 1.828e-03, size: 448, ETA: 2:47:24
2025-09-01 12:35:17.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 114/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, 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: 1.828e-03, size: 288, ETA: 2:47:20
2025-09-01 12:35:21.401 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 114/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.827e-03, size: 544, ETA: 2:47:18
2025-09-01 12:35:22.877 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:35:29.194 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:35:31.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:35:32.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5548
2025-09-01 12:35:33.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4855
2025-09-01 12:35:33.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2779
2025-09-01 12:35:33.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4394
2025-09-01 12:35:33.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:35:33.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:35:33.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.555
2025-09-01 12:35:33.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-09-01 12:35:33.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.278
2025-09-01 12:35:33.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.439
2025-09-01 12:35:33.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:35:33.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:35:33.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:35:33.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:35:33.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:35:33.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:35:33.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:35:33.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:35:33.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:35:35.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:35:37.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:35:39.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:35:40.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:35:42.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:35:44.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:35:46.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:35:48.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:35:50.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:35:50.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:35:50.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 12:35:50.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:35:50.261 | 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

2025-09-01 12:35:50.262 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:35:50.345 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:35:50.432 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch115
2025-09-01 12:35:53.618 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 115/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.827e-03, size: 416, ETA: 2:47:13
2025-09-01 12:35:56.912 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 115/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.826e-03, size: 384, ETA: 2:47:10
2025-09-01 12:36:00.238 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 115/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.826e-03, size: 256, ETA: 2:47:07
2025-09-01 12:36:03.437 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 115/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.825e-03, size: 480, ETA: 2:47:04
2025-09-01 12:36:06.713 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 115/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.2, lr: 1.825e-03, size: 480, ETA: 2:47:01
2025-09-01 12:36:09.978 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 115/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.162s, 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: 1.824e-03, size: 544, ETA: 2:46:58
2025-09-01 12:36:11.492 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:36:17.777 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:36:19.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:36:20.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5222
2025-09-01 12:36:21.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4157
2025-09-01 12:36:21.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2200
2025-09-01 12:36:21.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3860
2025-09-01 12:36:21.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:36:21.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:36:21.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-09-01 12:36:21.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-09-01 12:36:21.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.220
2025-09-01 12:36:21.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.386
2025-09-01 12:36:21.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:36:21.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:36:21.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:36:21.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:36:21.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:36:21.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:36:21.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:36:21.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:36:21.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:36:22.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:36:24.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:36:25.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:36:26.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:36:28.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:36:29.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:36:31.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:36:32.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:36:34.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:36:34.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-09-01 12:36:34.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-09-01 12:36:34.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:36:34.039 | 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: 7.04 ms

2025-09-01 12:36:34.041 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:36:34.118 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:36:34.204 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch116
2025-09-01 12:36:37.377 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 116/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.824e-03, size: 448, ETA: 2:46:53
2025-09-01 12:36:40.567 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 116/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.823e-03, size: 512, ETA: 2:46:50
2025-09-01 12:36:43.893 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 116/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.164s, data_time: 0.005s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.823e-03, size: 448, ETA: 2:46:47
2025-09-01 12:36:47.234 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 116/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.165s, 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: 1.822e-03, size: 576, ETA: 2:46:44
2025-09-01 12:36:50.548 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 116/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.822e-03, size: 384, ETA: 2:46:41
2025-09-01 12:36:53.843 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 116/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.821e-03, size: 512, ETA: 2:46:38
2025-09-01 12:36:55.367 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:37:01.573 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:37:03.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:37:04.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5586
2025-09-01 12:37:04.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4991
2025-09-01 12:37:04.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3098
2025-09-01 12:37:04.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4559
2025-09-01 12:37:04.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:37:04.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:37:04.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-09-01 12:37:04.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-09-01 12:37:04.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.310
2025-09-01 12:37:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.456
2025-09-01 12:37:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:37:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:37:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:37:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:37:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:37:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:37:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:37:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:37:04.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:37:06.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:37:07.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:37:08.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:37:10.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:37:11.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:37:13.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:37:14.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:37:15.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:37:17.181 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:37:17.181 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:37:17.182 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 12:37:17.182 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:37:17.192 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.90 ms, Average inference time: 6.99 ms

2025-09-01 12:37:17.193 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:37:17.270 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:37:17.357 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch117
2025-09-01 12:37:20.426 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 117/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.821e-03, size: 256, ETA: 2:46:33
2025-09-01 12:37:23.559 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 117/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.155s, 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: 1.820e-03, size: 352, ETA: 2:46:30
2025-09-01 12:37:27.001 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 117/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.169s, 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: 1.820e-03, size: 512, ETA: 2:46:27
2025-09-01 12:37:30.272 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 117/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.819e-03, size: 288, ETA: 2:46:24
2025-09-01 12:37:33.454 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 117/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.156s, 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: 1.819e-03, size: 416, ETA: 2:46:20
2025-09-01 12:37:36.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 117/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.2, lr: 1.818e-03, size: 544, ETA: 2:46:17
2025-09-01 12:37:38.119 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:37:44.344 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:37:46.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:37:48.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5257
2025-09-01 12:37:48.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4773
2025-09-01 12:37:48.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2401
2025-09-01 12:37:48.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4143
2025-09-01 12:37:48.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:37:48.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:37:48.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-09-01 12:37:48.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.477
2025-09-01 12:37:48.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.240
2025-09-01 12:37:48.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.414
2025-09-01 12:37:48.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:37:48.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:37:48.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:37:48.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:37:48.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:37:48.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:37:48.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:37:48.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:37:48.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:37:50.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:37:52.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:37:55.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:37:57.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:37:59.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:38:01.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:38:03.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:38:05.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:38:07.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:38:07.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 12:38:07.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-09-01 12:38:07.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:38:07.229 | 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

2025-09-01 12:38:07.230 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:38:07.348 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:38:07.466 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch118
2025-09-01 12:38:10.588 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 118/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.155s, 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: 1.818e-03, size: 544, ETA: 2:46:12
2025-09-01 12:38:13.762 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 118/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, 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: 1.817e-03, size: 512, ETA: 2:46:09
2025-09-01 12:38:17.031 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 118/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.817e-03, size: 416, ETA: 2:46:06
2025-09-01 12:38:20.298 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 118/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.816e-03, size: 448, ETA: 2:46:02
2025-09-01 12:38:23.591 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 118/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.162s, 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: 1.816e-03, size: 544, ETA: 2:45:59
2025-09-01 12:38:26.872 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 118/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.815e-03, size: 544, ETA: 2:45:56
2025-09-01 12:38:28.463 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:38:34.927 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:38:38.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:38:40.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5681
2025-09-01 12:38:41.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4712
2025-09-01 12:38:41.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2981
2025-09-01 12:38:41.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4458
2025-09-01 12:38:41.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:38:41.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:38:41.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.568
2025-09-01 12:38:41.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-09-01 12:38:41.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.298
2025-09-01 12:38:41.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.446
2025-09-01 12:38:41.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:38:41.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:38:41.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:38:41.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:38:41.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:38:41.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:38:41.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:38:41.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:38:41.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:38:44.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:38:46.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:38:49.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:38:52.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:38:55.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:38:58.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:39:01.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:39:04.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:39:06.863 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:39:06.863 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:39:06.864 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:39:06.864 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:39:06.889 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.97 ms, Average inference time: 7.08 ms

2025-09-01 12:39:06.891 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:39:06.978 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:39:07.071 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch119
2025-09-01 12:39:10.199 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 119/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.2, lr: 1.815e-03, size: 288, ETA: 2:45:52
2025-09-01 12:39:13.278 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 119/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.0, lr: 1.814e-03, size: 544, ETA: 2:45:48
2025-09-01 12:39:16.696 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 119/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.170s, data_time: 0.006s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.814e-03, size: 384, ETA: 2:45:45
2025-09-01 12:39:19.904 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 119/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, 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: 1.813e-03, size: 384, ETA: 2:45:42
2025-09-01 12:39:23.237 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 119/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.164s, 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: 1.813e-03, size: 480, ETA: 2:45:39
2025-09-01 12:39:26.541 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 119/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, 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: 1.812e-03, size: 448, ETA: 2:45:36
2025-09-01 12:39:28.073 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:39:34.255 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:39:36.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:39:38.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5349
2025-09-01 12:39:39.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4889
2025-09-01 12:39:39.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3150
2025-09-01 12:39:39.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4463
2025-09-01 12:39:39.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:39:39.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:39:39.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-09-01 12:39:39.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-09-01 12:39:39.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.315
2025-09-01 12:39:39.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.446
2025-09-01 12:39:39.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:39:39.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:39:39.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:39:39.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:39:39.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:39:39.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:39:39.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:39:39.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:39:39.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:39:41.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:39:44.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:39:46.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:39:48.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:39:51.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:39:53.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:39:55.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:39:57.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:39:59.751 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:39:59.751 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 12:39:59.751 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:39:59.751 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:39:59.776 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.96 ms, Average inference time: 7.23 ms

2025-09-01 12:39:59.778 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:39:59.926 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:40:00.015 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch120
2025-09-01 12:40:03.141 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 120/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.155s, 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: 1.812e-03, size: 480, ETA: 2:45:31
2025-09-01 12:40:06.334 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 120/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.811e-03, size: 576, ETA: 2:45:28
2025-09-01 12:40:09.697 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 120/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.811e-03, size: 416, ETA: 2:45:25
2025-09-01 12:40:12.907 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 120/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.810e-03, size: 352, ETA: 2:45:22
2025-09-01 12:40:16.161 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 120/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.810e-03, size: 448, ETA: 2:45:19
2025-09-01 12:40:19.368 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 120/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.2, lr: 1.809e-03, size: 480, ETA: 2:45:15
2025-09-01 12:40:20.832 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:40:27.095 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:40:29.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:40:31.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5561
2025-09-01 12:40:31.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5014
2025-09-01 12:40:31.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2922
2025-09-01 12:40:31.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4499
2025-09-01 12:40:31.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:40:31.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:40:31.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.556
2025-09-01 12:40:31.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-09-01 12:40:31.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.292
2025-09-01 12:40:31.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.450
2025-09-01 12:40:31.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:40:31.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:40:31.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:40:31.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:40:31.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:40:31.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:40:31.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:40:31.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:40:31.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:40:33.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:40:35.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:40:37.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:40:39.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:40:41.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:40:43.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:40:45.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:40:47.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:40:49.652 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:40:49.652 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:40:49.652 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:40:49.653 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:40:49.678 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.99 ms, Average inference time: 7.31 ms

2025-09-01 12:40:49.679 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:40:49.760 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:40:49.897 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch121
2025-09-01 12:40:53.018 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 121/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.155s, 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: 1.809e-03, size: 288, ETA: 2:45:10
2025-09-01 12:40:56.161 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 121/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.808e-03, size: 448, ETA: 2:45:07
2025-09-01 12:40:59.474 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 121/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 7.8, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.808e-03, size: 576, ETA: 2:45:04
2025-09-01 12:41:02.751 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 121/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, 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: 1.807e-03, size: 256, ETA: 2:45:01
2025-09-01 12:41:05.968 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 121/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.807e-03, size: 576, ETA: 2:44:58
2025-09-01 12:41:09.273 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 121/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.806e-03, size: 320, ETA: 2:44:55
2025-09-01 12:41:10.689 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:41:16.957 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:41:22.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:41:26.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5384
2025-09-01 12:41:27.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4917
2025-09-01 12:41:27.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3401
2025-09-01 12:41:27.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4568
2025-09-01 12:41:27.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:41:27.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:41:27.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.538
2025-09-01 12:41:27.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.492
2025-09-01 12:41:27.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-09-01 12:41:27.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.457
2025-09-01 12:41:27.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:41:27.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:41:27.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:41:27.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:41:27.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:41:27.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:41:27.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:41:27.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:41:27.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:41:32.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:41:36.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:41:41.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:41:46.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:41:51.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:41:56.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:42:01.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:42:06.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:42:10.939 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:42:10.939 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:42:10.939 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 12:42:10.939 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:42:10.971 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 1.00 ms, Average inference time: 7.12 ms

2025-09-01 12:42:10.972 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:42:11.082 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:42:11.182 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch122
2025-09-01 12:42:14.284 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 122/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.154s, 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: 1.805e-03, size: 256, ETA: 2:44:49
2025-09-01 12:42:17.577 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 122/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, 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: 1.805e-03, size: 320, ETA: 2:44:46
2025-09-01 12:42:20.922 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 122/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.165s, 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: 1.804e-03, size: 416, ETA: 2:44:44
2025-09-01 12:42:24.164 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 122/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.160s, 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: 1.804e-03, size: 320, ETA: 2:44:40
2025-09-01 12:42:27.495 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 122/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.164s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.804e-03, size: 352, ETA: 2:44:37
2025-09-01 12:42:30.642 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 122/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.803e-03, size: 384, ETA: 2:44:34
2025-09-01 12:42:32.146 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:42:38.526 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:42:40.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:42:42.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5657
2025-09-01 12:42:42.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4729
2025-09-01 12:42:42.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2808
2025-09-01 12:42:42.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4398
2025-09-01 12:42:42.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:42:42.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:42:42.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.566
2025-09-01 12:42:42.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.473
2025-09-01 12:42:42.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.281
2025-09-01 12:42:42.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.440
2025-09-01 12:42:42.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:42:42.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:42:42.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:42:42.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:42:42.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:42:42.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:42:42.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:42:42.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:42:42.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:42:44.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:42:46.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:42:48.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:42:50.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:42:52.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:42:54.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:42:56.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:42:58.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:43:00.145 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:43:00.145 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:43:00.145 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 12:43:00.145 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:43:00.173 | 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

2025-09-01 12:43:00.174 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:43:00.254 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:43:00.340 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch123
2025-09-01 12:43:03.529 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 123/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.802e-03, size: 384, ETA: 2:44:29
2025-09-01 12:43:06.869 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 123/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.165s, 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: 1.802e-03, size: 448, ETA: 2:44:26
2025-09-01 12:43:10.102 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 123/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 6.7, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.801e-03, size: 256, ETA: 2:44:23
2025-09-01 12:43:13.357 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 123/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.160s, 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: 1.801e-03, size: 320, ETA: 2:44:20
2025-09-01 12:43:16.653 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 123/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.162s, 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: 1.800e-03, size: 480, ETA: 2:44:17
2025-09-01 12:43:19.963 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 123/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.800e-03, size: 288, ETA: 2:44:14
2025-09-01 12:43:21.483 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:43:27.726 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:43:29.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:43:30.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5268
2025-09-01 12:43:30.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4286
2025-09-01 12:43:30.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1992
2025-09-01 12:43:30.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3849
2025-09-01 12:43:30.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:43:30.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:43:30.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-09-01 12:43:30.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.429
2025-09-01 12:43:30.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.199
2025-09-01 12:43:30.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.385
2025-09-01 12:43:30.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:43:30.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:43:30.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:43:30.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:43:30.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:43:30.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:43:30.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:43:30.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:43:30.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:43:31.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:43:33.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:43:34.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:43:35.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:43:36.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:43:37.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:43:39.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:43:40.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:43:41.705 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:43:41.705 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.16
2025-09-01 12:43:41.705 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-09-01 12:43:41.705 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:43:41.713 | 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

2025-09-01 12:43:41.714 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:43:41.797 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:43:41.892 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch124
2025-09-01 12:43:45.158 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 124/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.161s, 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: 1.799e-03, size: 256, ETA: 2:44:09
2025-09-01 12:43:48.492 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 124/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.3, lr: 1.799e-03, size: 384, ETA: 2:44:06
2025-09-01 12:43:51.645 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 124/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.798e-03, size: 448, ETA: 2:44:03
2025-09-01 12:43:54.855 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 124/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, 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: 1.798e-03, size: 256, ETA: 2:44:00
2025-09-01 12:43:57.935 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 124/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.797e-03, size: 320, ETA: 2:43:56
2025-09-01 12:44:01.227 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 124/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, 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: 1.797e-03, size: 544, ETA: 2:43:53
2025-09-01 12:44:02.707 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:44:09.052 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:44:10.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:44:12.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5715
2025-09-01 12:44:12.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5032
2025-09-01 12:44:12.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2755
2025-09-01 12:44:12.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4501
2025-09-01 12:44:12.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:44:12.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:44:12.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-09-01 12:44:12.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-09-01 12:44:12.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-09-01 12:44:12.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.450
2025-09-01 12:44:12.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:44:12.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:44:12.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:44:12.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:44:12.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:44:12.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:44:12.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:44:12.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:44:12.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:44:14.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:44:15.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:44:17.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:44:18.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:44:20.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:44:22.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:44:23.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:44:25.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:44:26.903 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:44:26.903 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:44:26.904 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:44:26.904 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:44:26.928 | 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: 7.21 ms

2025-09-01 12:44:26.930 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:44:27.018 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:44:27.110 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch125
2025-09-01 12:44:30.219 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 125/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.153s, 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: 1.796e-03, size: 256, ETA: 2:43:48
2025-09-01 12:44:33.438 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 125/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.796e-03, size: 384, ETA: 2:43:44
2025-09-01 12:44:36.843 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 125/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.167s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.795e-03, size: 480, ETA: 2:43:42
2025-09-01 12:44:40.105 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 125/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.161s, 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: 1.795e-03, size: 512, ETA: 2:43:39
2025-09-01 12:44:43.274 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 125/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, 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: 0.9, lr: 1.794e-03, size: 288, ETA: 2:43:35
2025-09-01 12:44:46.460 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 125/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.2, lr: 1.794e-03, size: 352, ETA: 2:43:32
2025-09-01 12:44:47.835 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:44:54.194 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:44:57.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:45:00.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5548
2025-09-01 12:45:00.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4791
2025-09-01 12:45:00.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2746
2025-09-01 12:45:01.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4362
2025-09-01 12:45:01.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:45:01.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:45:01.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.555
2025-09-01 12:45:01.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-09-01 12:45:01.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-09-01 12:45:01.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.436
2025-09-01 12:45:01.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:45:01.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:45:01.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:45:01.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:45:01.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:45:01.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:45:01.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:45:01.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:45:01.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:45:03.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:45:06.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:45:09.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:45:12.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:45:15.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:45:18.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:45:21.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:45:24.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:45:27.568 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:45:27.568 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:45:27.569 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 12:45:27.569 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:45:27.596 | 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

2025-09-01 12:45:27.597 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:45:27.683 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:45:27.770 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch126
2025-09-01 12:45:30.882 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 126/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.0, lr: 1.793e-03, size: 512, ETA: 2:43:26
2025-09-01 12:45:34.179 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 126/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.792e-03, size: 320, ETA: 2:43:23
2025-09-01 12:45:37.491 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 126/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.792e-03, size: 416, ETA: 2:43:20
2025-09-01 12:45:40.676 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 126/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.157s, 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: 1.791e-03, size: 544, ETA: 2:43:17
2025-09-01 12:45:43.987 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 126/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.791e-03, size: 576, ETA: 2:43:14
2025-09-01 12:45:47.377 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 126/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.167s, 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: 1.790e-03, size: 576, ETA: 2:43:11
2025-09-01 12:45:48.866 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:45:55.180 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:45:58.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:46:00.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5656
2025-09-01 12:46:00.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4919
2025-09-01 12:46:00.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3119
2025-09-01 12:46:00.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4564
2025-09-01 12:46:00.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:46:00.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:46:00.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.566
2025-09-01 12:46:00.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.492
2025-09-01 12:46:00.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-09-01 12:46:00.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.456
2025-09-01 12:46:00.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:46:00.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:46:00.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:46:00.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:46:00.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:46:00.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:46:00.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:46:00.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:46:00.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:46:03.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:46:05.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:46:07.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:46:10.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:46:12.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:46:15.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:46:17.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:46:20.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:46:22.453 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:46:22.454 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:46:22.454 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 12:46:22.454 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:46:22.483 | 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

2025-09-01 12:46:22.484 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:46:22.607 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:46:22.715 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch127
2025-09-01 12:46:25.755 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 127/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.790e-03, size: 320, ETA: 2:43:06
2025-09-01 12:46:29.114 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 127/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.789e-03, size: 448, ETA: 2:43:03
2025-09-01 12:46:32.531 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 127/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.168s, data_time: 0.003s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.789e-03, size: 576, ETA: 2:43:00
2025-09-01 12:46:35.866 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 127/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.788e-03, size: 320, ETA: 2:42:57
2025-09-01 12:46:39.037 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 127/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.157s, 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: 1.788e-03, size: 384, ETA: 2:42:54
2025-09-01 12:46:42.190 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 127/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.787e-03, size: 352, ETA: 2:42:50
2025-09-01 12:46:43.587 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:46:49.853 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:46:51.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:46:52.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5378
2025-09-01 12:46:52.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4557
2025-09-01 12:46:52.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2956
2025-09-01 12:46:52.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4297
2025-09-01 12:46:52.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:46:52.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:46:52.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.538
2025-09-01 12:46:52.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.456
2025-09-01 12:46:52.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.296
2025-09-01 12:46:52.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.430
2025-09-01 12:46:52.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:46:52.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:46:52.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:46:52.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:46:52.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:46:52.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:46:52.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:46:52.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:46:52.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:46:54.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:46:55.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:46:56.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:46:58.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:46:59.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:47:00.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:47:01.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:47:03.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:47:04.423 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:47:04.424 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-09-01 12:47:04.424 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 12:47:04.424 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:47:04.432 | 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: 7.15 ms

2025-09-01 12:47:04.433 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:47:04.603 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:47:04.700 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch128
2025-09-01 12:47:07.812 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 128/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.786e-03, size: 544, ETA: 2:42:45
2025-09-01 12:47:11.084 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 128/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.786e-03, size: 352, ETA: 2:42:42
2025-09-01 12:47:14.313 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 128/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, 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: 1.785e-03, size: 544, ETA: 2:42:39
2025-09-01 12:47:17.592 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 128/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.161s, 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: 1.785e-03, size: 320, ETA: 2:42:35
2025-09-01 12:47:20.773 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 128/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.157s, 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: 1.784e-03, size: 544, ETA: 2:42:32
2025-09-01 12:47:23.958 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 128/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.157s, 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: 1.784e-03, size: 288, ETA: 2:42:29
2025-09-01 12:47:25.391 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:47:31.626 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:47:34.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:47:37.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5781
2025-09-01 12:47:37.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5017
2025-09-01 12:47:37.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2962
2025-09-01 12:47:37.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4586
2025-09-01 12:47:37.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:47:37.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:47:37.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-09-01 12:47:37.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-09-01 12:47:37.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.296
2025-09-01 12:47:37.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.459
2025-09-01 12:47:37.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:47:37.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:47:37.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:47:37.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:47:37.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:47:37.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:47:37.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:47:37.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:47:37.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:47:39.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:47:42.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:47:45.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:47:47.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:47:50.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:47:52.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:47:55.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:47:58.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:48:00.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:48:00.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 12:48:00.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 12:48:00.628 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:48:00.660 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.97 ms, Average inference time: 7.09 ms

2025-09-01 12:48:00.661 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:48:00.785 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:48:00.904 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch129
2025-09-01 12:48:04.032 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 129/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.783e-03, size: 384, ETA: 2:42:23
2025-09-01 12:48:07.267 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 129/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, 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: 1.783e-03, size: 416, ETA: 2:42:20
2025-09-01 12:48:10.569 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 129/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.782e-03, size: 320, ETA: 2:42:17
2025-09-01 12:48:13.742 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 129/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 6.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.782e-03, size: 288, ETA: 2:42:14
2025-09-01 12:48:16.978 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 129/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, 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: 1.781e-03, size: 512, ETA: 2:42:10
2025-09-01 12:48:20.272 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 129/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.162s, 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: 1.781e-03, size: 384, ETA: 2:42:07
2025-09-01 12:48:21.676 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:48:28.013 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:48:31.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:48:33.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5445
2025-09-01 12:48:34.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4676
2025-09-01 12:48:34.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3145
2025-09-01 12:48:34.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4422
2025-09-01 12:48:34.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:48:34.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:48:34.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.544
2025-09-01 12:48:34.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-09-01 12:48:34.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-09-01 12:48:34.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.442
2025-09-01 12:48:34.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:48:34.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:48:34.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:48:34.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:48:34.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:48:34.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:48:34.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:48:34.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:48:34.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:48:36.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:48:39.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:48:42.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:48:45.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:48:48.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:48:50.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:48:53.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:48:56.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:48:59.296 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:48:59.296 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:48:59.296 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 12:48:59.297 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:48:59.322 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.99 ms, Average inference time: 7.16 ms

2025-09-01 12:48:59.323 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:48:59.402 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:48:59.540 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch130
2025-09-01 12:49:02.955 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 130/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.168s, 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: 1.780e-03, size: 384, ETA: 2:42:03
2025-09-01 12:49:06.289 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 130/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.8, lr: 1.779e-03, size: 576, ETA: 2:42:00
2025-09-01 12:49:09.951 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 130/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.180s, data_time: 0.003s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.779e-03, size: 256, ETA: 2:41:59
2025-09-01 12:49:13.324 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 130/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.166s, 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: 1.778e-03, size: 576, ETA: 2:41:56
2025-09-01 12:49:17.018 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 130/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.182s, data_time: 0.003s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.778e-03, size: 352, ETA: 2:41:54
2025-09-01 12:49:20.399 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 130/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.167s, 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: 1.777e-03, size: 448, ETA: 2:41:51
2025-09-01 12:49:21.924 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:49:28.194 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:49:30.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:49:31.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5597
2025-09-01 12:49:32.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4980
2025-09-01 12:49:32.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3193
2025-09-01 12:49:32.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4590
2025-09-01 12:49:32.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:49:32.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:49:32.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.560
2025-09-01 12:49:32.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-09-01 12:49:32.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.319
2025-09-01 12:49:32.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.459
2025-09-01 12:49:32.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:49:32.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:49:32.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:49:32.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:49:32.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:49:32.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:49:32.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:49:32.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:49:32.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:49:33.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:49:35.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:49:37.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:49:39.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:49:41.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:49:42.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:49:44.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:49:46.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:49:48.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:49:48.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 12:49:48.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 12:49:48.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:49:48.220 | 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

2025-09-01 12:49:48.224 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:49:48.306 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:49:48.394 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch131
2025-09-01 12:49:51.593 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 131/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.1, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.777e-03, size: 288, ETA: 2:41:47
2025-09-01 12:49:54.852 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 131/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.776e-03, size: 512, ETA: 2:41:44
2025-09-01 12:49:58.129 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 131/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.161s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.776e-03, size: 576, ETA: 2:41:41
2025-09-01 12:50:01.429 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 131/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 9.1, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.7, cls_loss: 1.1, lr: 1.775e-03, size: 512, ETA: 2:41:38
2025-09-01 12:50:04.669 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 131/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.775e-03, size: 384, ETA: 2:41:34
2025-09-01 12:50:07.982 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 131/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.774e-03, size: 576, ETA: 2:41:31
2025-09-01 12:50:09.440 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:50:15.898 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:50:19.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:50:21.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5389
2025-09-01 12:50:22.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4666
2025-09-01 12:50:22.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3245
2025-09-01 12:50:22.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4433
2025-09-01 12:50:22.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:50:22.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:50:22.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-09-01 12:50:22.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.467
2025-09-01 12:50:22.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.325
2025-09-01 12:50:22.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.443
2025-09-01 12:50:22.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:50:22.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:50:22.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:50:22.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:50:22.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:50:22.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:50:22.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:50:22.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:50:22.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:50:25.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:50:28.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:50:31.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:50:33.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:50:36.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:50:39.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:50:42.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:50:45.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:50:48.218 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:50:48.218 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:50:48.218 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 12:50:48.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:50:48.245 | 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

2025-09-01 12:50:48.247 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:50:48.328 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:50:48.423 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch132
2025-09-01 12:50:51.520 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 132/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.153s, 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: 1.773e-03, size: 480, ETA: 2:41:26
2025-09-01 12:50:54.823 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 132/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.163s, 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: 1.773e-03, size: 512, ETA: 2:41:23
2025-09-01 12:50:58.134 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 132/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.164s, 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: 1.772e-03, size: 544, ETA: 2:41:20
2025-09-01 12:51:01.293 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 132/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.156s, 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: 1.772e-03, size: 416, ETA: 2:41:17
2025-09-01 12:51:04.506 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 132/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.0Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.771e-03, size: 352, ETA: 2:41:13
2025-09-01 12:51:07.851 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 132/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.771e-03, size: 480, ETA: 2:41:10
2025-09-01 12:51:09.268 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:51:15.630 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:51:19.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:51:21.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5665
2025-09-01 12:51:22.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5073
2025-09-01 12:51:22.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2861
2025-09-01 12:51:22.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4533
2025-09-01 12:51:22.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:51:22.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:51:22.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.566
2025-09-01 12:51:22.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-09-01 12:51:22.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.286
2025-09-01 12:51:22.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.453
2025-09-01 12:51:22.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:51:22.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:51:22.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:51:22.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:51:22.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:51:22.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:51:22.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:51:22.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:51:22.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:51:25.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:51:28.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:51:31.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:51:34.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:51:37.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:51:40.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:51:43.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:51:46.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:51:49.104 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:51:49.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 12:51:49.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:51:49.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:51:49.131 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.33 ms, Average NMS time: 0.99 ms, Average inference time: 7.32 ms

2025-09-01 12:51:49.132 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:51:49.247 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:51:49.334 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch133
2025-09-01 12:51:52.477 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 133/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.770e-03, size: 320, ETA: 2:41:05
2025-09-01 12:51:55.662 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 133/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.769e-03, size: 256, ETA: 2:41:02
2025-09-01 12:51:59.021 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 133/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.769e-03, size: 448, ETA: 2:40:59
2025-09-01 12:52:02.298 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 133/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.1, lr: 1.768e-03, size: 320, ETA: 2:40:56
2025-09-01 12:52:05.489 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 133/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, 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: 1.0, lr: 1.768e-03, size: 416, ETA: 2:40:53
2025-09-01 12:52:08.645 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 133/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.767e-03, size: 352, ETA: 2:40:49
2025-09-01 12:52:10.083 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:52:16.343 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:52:18.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:52:19.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5354
2025-09-01 12:52:19.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4728
2025-09-01 12:52:20.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2848
2025-09-01 12:52:20.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4310
2025-09-01 12:52:20.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:52:20.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:52:20.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-09-01 12:52:20.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.473
2025-09-01 12:52:20.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.285
2025-09-01 12:52:20.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.431
2025-09-01 12:52:20.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:52:20.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:52:20.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:52:20.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:52:20.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:52:20.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:52:20.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:52:20.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:52:20.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:52:21.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:52:23.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:52:25.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:52:26.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:52:28.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:52:30.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:52:31.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:52:33.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:52:35.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:52:35.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:52:35.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 12:52:35.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:52:35.398 | 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: 7.10 ms

2025-09-01 12:52:35.400 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:52:35.480 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:52:35.564 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch134
2025-09-01 12:52:38.843 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 134/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.162s, 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: 1.767e-03, size: 576, ETA: 2:40:45
2025-09-01 12:52:42.109 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 134/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.766e-03, size: 288, ETA: 2:40:41
2025-09-01 12:52:45.493 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 134/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.167s, 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: 1.766e-03, size: 512, ETA: 2:40:39
2025-09-01 12:52:48.751 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 134/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.765e-03, size: 576, ETA: 2:40:36
2025-09-01 12:52:51.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 134/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.159s, 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: 1.765e-03, size: 256, ETA: 2:40:32
2025-09-01 12:52:55.344 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 134/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.167s, 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: 1.764e-03, size: 384, ETA: 2:40:29
2025-09-01 12:52:56.782 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:53:03.148 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:53:06.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:53:08.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5641
2025-09-01 12:53:08.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4931
2025-09-01 12:53:08.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3274
2025-09-01 12:53:08.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4615
2025-09-01 12:53:08.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:53:08.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:53:08.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.564
2025-09-01 12:53:08.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-09-01 12:53:08.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.327
2025-09-01 12:53:08.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.462
2025-09-01 12:53:08.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:53:08.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:53:08.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:53:08.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:53:08.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:53:08.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:53:08.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:53:08.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:53:08.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:53:11.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:53:14.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:53:16.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:53:19.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:53:22.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:53:24.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:53:27.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:53:29.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:53:32.504 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:53:32.504 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 12:53:32.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 12:53:32.505 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:53:32.532 | 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

2025-09-01 12:53:32.533 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:53:32.659 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:53:32.753 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch135
2025-09-01 12:53:35.759 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 135/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.763e-03, size: 416, ETA: 2:40:24
2025-09-01 12:53:38.893 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 135/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.155s, 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: 1.763e-03, size: 320, ETA: 2:40:20
2025-09-01 12:53:42.166 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 135/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.762e-03, size: 544, ETA: 2:40:17
2025-09-01 12:53:45.385 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 135/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, 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: 1.762e-03, size: 576, ETA: 2:40:14
2025-09-01 12:53:48.674 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 135/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.761e-03, size: 288, ETA: 2:40:11
2025-09-01 12:53:51.949 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 135/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.161s, 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: 1.761e-03, size: 448, ETA: 2:40:08
2025-09-01 12:53:53.463 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:54:00.002 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:54:02.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:54:05.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5303
2025-09-01 12:54:05.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4641
2025-09-01 12:54:05.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2580
2025-09-01 12:54:05.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4175
2025-09-01 12:54:05.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:54:05.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:54:05.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-09-01 12:54:05.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.464
2025-09-01 12:54:05.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.258
2025-09-01 12:54:05.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.417
2025-09-01 12:54:05.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:54:05.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:54:05.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:54:05.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:54:05.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:54:05.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:54:05.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:54:05.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:54:05.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:54:07.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:54:10.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:54:12.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:54:14.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:54:17.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:54:19.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:54:22.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:54:24.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:54:26.724 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:54:26.724 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 12:54:26.725 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-09-01 12:54:26.725 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:54:26.751 | 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

2025-09-01 12:54:26.752 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:54:26.895 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:54:26.990 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch136
2025-09-01 12:54:30.164 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 136/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.760e-03, size: 448, ETA: 2:40:03
2025-09-01 12:54:33.378 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 136/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.759e-03, size: 384, ETA: 2:40:00
2025-09-01 12:54:36.568 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 136/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.156s, 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: 1.759e-03, size: 352, ETA: 2:39:56
2025-09-01 12:54:39.786 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 136/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, 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: 1.758e-03, size: 576, ETA: 2:39:53
2025-09-01 12:54:43.165 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 136/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.758e-03, size: 480, ETA: 2:39:50
2025-09-01 12:54:46.397 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 136/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.757e-03, size: 352, ETA: 2:39:47
2025-09-01 12:54:47.888 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:54:54.284 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:54:57.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:55:00.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5591
2025-09-01 12:55:00.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4906
2025-09-01 12:55:00.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2718
2025-09-01 12:55:00.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4405
2025-09-01 12:55:00.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:55:00.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:55:00.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-09-01 12:55:00.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-09-01 12:55:00.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.272
2025-09-01 12:55:00.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.440
2025-09-01 12:55:00.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:55:00.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:55:00.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:55:00.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:55:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:55:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:55:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:55:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:55:00.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:55:03.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:55:06.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:55:09.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:55:12.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:55:15.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:55:18.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:55:21.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:55:24.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:55:27.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:55:27.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 12:55:27.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 12:55:27.886 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:55:27.914 | 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

2025-09-01 12:55:27.915 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:55:27.994 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:55:28.129 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch137
2025-09-01 12:55:31.452 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 137/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.165s, 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: 1.756e-03, size: 416, ETA: 2:39:43
2025-09-01 12:55:34.664 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 137/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.756e-03, size: 480, ETA: 2:39:39
2025-09-01 12:55:37.892 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 137/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.1, lr: 1.755e-03, size: 480, ETA: 2:39:36
2025-09-01 12:55:41.086 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 137/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.157s, 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: 1.755e-03, size: 480, ETA: 2:39:33
2025-09-01 12:55:44.325 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 137/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.161s, 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: 1.754e-03, size: 416, ETA: 2:39:29
2025-09-01 12:55:47.625 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 137/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.0, lr: 1.754e-03, size: 416, ETA: 2:39:26
2025-09-01 12:55:49.097 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:55:55.276 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:55:58.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:56:01.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5560
2025-09-01 12:56:01.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4957
2025-09-01 12:56:01.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3255
2025-09-01 12:56:01.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4591
2025-09-01 12:56:01.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:56:01.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:56:01.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.556
2025-09-01 12:56:01.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-09-01 12:56:01.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.326
2025-09-01 12:56:01.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.459
2025-09-01 12:56:01.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:56:01.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:56:01.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:56:01.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:56:01.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:56:01.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:56:01.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:56:01.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:56:01.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:56:04.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:56:06.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:56:09.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:56:12.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:56:15.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:56:17.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:56:20.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:56:23.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:56:26.283 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:56:26.283 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 12:56:26.283 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 12:56:26.284 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:56:26.311 | 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

2025-09-01 12:56:26.312 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:56:26.399 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:56:26.488 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch138
2025-09-01 12:56:29.629 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 138/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.155s, 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: 1.753e-03, size: 416, ETA: 2:39:21
2025-09-01 12:56:33.063 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 138/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.7, lr: 1.752e-03, size: 288, ETA: 2:39:19
2025-09-01 12:56:36.282 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 138/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, 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: 1.752e-03, size: 416, ETA: 2:39:15
2025-09-01 12:56:39.509 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 138/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, 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: 1.751e-03, size: 512, ETA: 2:39:12
2025-09-01 12:56:42.805 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 138/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.751e-03, size: 448, ETA: 2:39:09
2025-09-01 12:56:45.968 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 138/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.750e-03, size: 320, ETA: 2:39:06
2025-09-01 12:56:47.430 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:56:53.633 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:56:55.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:56:57.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5541
2025-09-01 12:56:57.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4789
2025-09-01 12:56:58.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3069
2025-09-01 12:56:58.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4466
2025-09-01 12:56:58.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:56:58.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:56:58.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.554
2025-09-01 12:56:58.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-09-01 12:56:58.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.307
2025-09-01 12:56:58.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.447
2025-09-01 12:56:58.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:56:58.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:56:58.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:56:58.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:56:58.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:56:58.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:56:58.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:56:58.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:56:58.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:57:00.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:57:02.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:57:03.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:57:05.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:57:07.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:57:09.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:57:11.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:57:13.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:57:15.766 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:57:15.766 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 12:57:15.766 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 12:57:15.766 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:57:15.792 | 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

2025-09-01 12:57:15.793 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:57:15.874 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:57:15.960 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch139
2025-09-01 12:57:19.130 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 139/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.750e-03, size: 512, ETA: 2:39:01
2025-09-01 12:57:22.370 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 139/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.7, lr: 1.749e-03, size: 320, ETA: 2:38:58
2025-09-01 12:57:25.572 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 139/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.748e-03, size: 544, ETA: 2:38:54
2025-09-01 12:57:28.794 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 139/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.748e-03, size: 544, ETA: 2:38:51
2025-09-01 12:57:32.133 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 139/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.747e-03, size: 256, ETA: 2:38:48
2025-09-01 12:57:35.241 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 139/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.747e-03, size: 416, ETA: 2:38:44
2025-09-01 12:57:36.666 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:57:42.858 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:57:45.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:57:47.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5330
2025-09-01 12:57:47.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4813
2025-09-01 12:57:47.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2985
2025-09-01 12:57:47.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4376
2025-09-01 12:57:47.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:57:47.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:57:47.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-09-01 12:57:47.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-09-01 12:57:47.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.299
2025-09-01 12:57:47.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.438
2025-09-01 12:57:47.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:57:47.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:57:47.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:57:47.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:57:47.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:57:47.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:57:47.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:57:47.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:57:47.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:57:49.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:57:51.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:57:53.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:57:55.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:57:57.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:57:59.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:58:01.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:58:03.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:58:05.794 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:58:05.794 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:58:05.794 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 12:58:05.794 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:58:05.821 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.99 ms, Average inference time: 7.16 ms

2025-09-01 12:58:05.822 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:58:05.957 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:58:06.046 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch140
2025-09-01 12:58:09.191 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 140/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.746e-03, size: 416, ETA: 2:38:39
2025-09-01 12:58:12.379 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 140/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.746e-03, size: 544, ETA: 2:38:36
2025-09-01 12:58:15.715 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 140/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.745e-03, size: 256, ETA: 2:38:33
2025-09-01 12:58:18.904 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 140/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.157s, 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: 1.744e-03, size: 416, ETA: 2:38:30
2025-09-01 12:58:22.192 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 140/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.163s, 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: 1.744e-03, size: 544, ETA: 2:38:27
2025-09-01 12:58:25.523 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 140/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.743e-03, size: 576, ETA: 2:38:24
2025-09-01 12:58:27.064 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:58:33.421 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:58:35.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:58:36.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5486
2025-09-01 12:58:36.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5040
2025-09-01 12:58:36.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2733
2025-09-01 12:58:36.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4419
2025-09-01 12:58:36.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:58:36.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:58:36.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.549
2025-09-01 12:58:36.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-09-01 12:58:36.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.273
2025-09-01 12:58:36.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.442
2025-09-01 12:58:36.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:58:36.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:58:36.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:58:36.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:58:36.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:58:36.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:58:36.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:58:36.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:58:36.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:58:38.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:58:39.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:58:41.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:58:42.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:58:43.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:58:45.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:58:46.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:58:48.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:58:49.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:58:49.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 12:58:49.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 12:58:49.771 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:58:49.781 | 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

2025-09-01 12:58:49.782 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:58:49.860 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:58:49.950 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch141
2025-09-01 12:58:53.036 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 141/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.743e-03, size: 416, ETA: 2:38:19
2025-09-01 12:58:56.220 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 141/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.742e-03, size: 288, ETA: 2:38:15
2025-09-01 12:58:59.568 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 141/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.742e-03, size: 416, ETA: 2:38:12
2025-09-01 12:59:02.771 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 141/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.741e-03, size: 448, ETA: 2:38:09
2025-09-01 12:59:06.016 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 141/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.159s, 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: 1.740e-03, size: 384, ETA: 2:38:06
2025-09-01 12:59:09.359 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 141/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.740e-03, size: 448, ETA: 2:38:03
2025-09-01 12:59:10.871 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:59:17.128 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 12:59:19.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 12:59:20.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5435
2025-09-01 12:59:20.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4838
2025-09-01 12:59:20.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2644
2025-09-01 12:59:20.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4305
2025-09-01 12:59:20.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 12:59:20.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 12:59:20.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-09-01 12:59:20.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-09-01 12:59:20.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.264
2025-09-01 12:59:20.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.431
2025-09-01 12:59:20.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 12:59:20.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 12:59:20.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 12:59:20.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 12:59:20.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 12:59:20.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 12:59:20.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 12:59:20.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 12:59:20.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 12:59:22.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 12:59:24.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 12:59:25.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 12:59:27.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 12:59:29.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 12:59:30.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 12:59:32.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 12:59:34.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 12:59:35.894 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 12:59:35.894 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 12:59:35.894 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 12:59:35.895 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 12:59:35.923 | 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: 7.23 ms

2025-09-01 12:59:35.926 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:59:36.060 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 12:59:36.148 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch142
2025-09-01 12:59:39.277 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 142/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.739e-03, size: 576, ETA: 2:37:58
2025-09-01 12:59:42.533 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 142/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.739e-03, size: 416, ETA: 2:37:55
2025-09-01 12:59:45.819 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 142/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.3, lr: 1.738e-03, size: 256, ETA: 2:37:52
2025-09-01 12:59:49.027 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 142/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, 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: 1.737e-03, size: 384, ETA: 2:37:49
2025-09-01 12:59:52.293 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 142/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.161s, 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: 1.737e-03, size: 384, ETA: 2:37:45
2025-09-01 12:59:55.480 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 142/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.157s, 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: 1.736e-03, size: 320, ETA: 2:37:42
2025-09-01 12:59:56.966 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:00:03.149 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:00:05.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:00:07.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5569
2025-09-01 13:00:07.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4908
2025-09-01 13:00:07.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2763
2025-09-01 13:00:07.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4413
2025-09-01 13:00:07.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:00:07.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:00:07.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.557
2025-09-01 13:00:07.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-09-01 13:00:07.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.276
2025-09-01 13:00:07.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.441
2025-09-01 13:00:07.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:00:07.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:00:07.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:00:07.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:00:07.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:00:07.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:00:07.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:00:07.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:00:07.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:00:09.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:00:11.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:00:13.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:00:15.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:00:16.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:00:18.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:00:20.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:00:22.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:00:24.520 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:00:24.520 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:00:24.520 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:00:24.521 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:00:24.547 | 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

2025-09-01 13:00:24.548 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:00:24.656 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:00:24.762 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch143
2025-09-01 13:00:27.971 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 143/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.159s, 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: 1.736e-03, size: 320, ETA: 2:37:37
2025-09-01 13:00:31.168 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 143/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 7.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.735e-03, size: 256, ETA: 2:37:34
2025-09-01 13:00:34.363 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 143/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.734e-03, size: 320, ETA: 2:37:31
2025-09-01 13:00:37.730 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 143/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.734e-03, size: 384, ETA: 2:37:28
2025-09-01 13:00:40.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 143/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.733e-03, size: 480, ETA: 2:37:24
2025-09-01 13:00:44.228 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 143/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.733e-03, size: 384, ETA: 2:37:21
2025-09-01 13:00:45.701 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:00:52.037 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:00:54.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:00:55.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4974
2025-09-01 13:00:55.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4756
2025-09-01 13:00:55.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3038
2025-09-01 13:00:55.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4256
2025-09-01 13:00:55.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:00:55.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:00:55.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-09-01 13:00:55.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-09-01 13:00:55.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-09-01 13:00:55.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.426
2025-09-01 13:00:55.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:00:55.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:00:55.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:00:55.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:00:55.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:00:55.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:00:55.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:00:55.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:00:55.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:00:57.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:00:59.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:01:00.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:01:02.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:01:03.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:01:05.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:01:06.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:01:08.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:01:09.854 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:01:09.854 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 13:01:09.855 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 13:01:09.855 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:01:09.881 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.35 ms, Average NMS time: 0.96 ms, Average inference time: 7.31 ms

2025-09-01 13:01:09.882 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:01:09.963 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:01:10.053 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch144
2025-09-01 13:01:13.162 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 144/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.154s, 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: 1.732e-03, size: 352, ETA: 2:37:16
2025-09-01 13:01:16.439 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 144/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.731e-03, size: 352, ETA: 2:37:13
2025-09-01 13:01:19.690 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 144/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.160s, 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: 1.731e-03, size: 288, ETA: 2:37:10
2025-09-01 13:01:22.918 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 144/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.159s, 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: 1.730e-03, size: 512, ETA: 2:37:07
2025-09-01 13:01:26.278 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 144/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.9, lr: 1.730e-03, size: 320, ETA: 2:37:04
2025-09-01 13:01:29.627 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 144/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.165s, 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: 1.729e-03, size: 416, ETA: 2:37:01
2025-09-01 13:01:31.087 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:01:37.344 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:01:39.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:01:41.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5577
2025-09-01 13:01:41.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4756
2025-09-01 13:01:41.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2587
2025-09-01 13:01:41.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4307
2025-09-01 13:01:41.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:01:41.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:01:41.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.558
2025-09-01 13:01:41.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-09-01 13:01:41.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.259
2025-09-01 13:01:41.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.431
2025-09-01 13:01:41.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:01:41.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:01:41.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:01:41.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:01:41.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:01:41.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:01:41.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:01:41.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:01:41.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:01:43.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:01:45.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:01:47.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:01:49.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:01:51.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:01:52.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:01:54.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:01:56.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:01:58.671 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:01:58.671 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:01:58.672 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 13:01:58.672 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:01:58.697 | 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

2025-09-01 13:01:58.703 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:01:58.786 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:01:58.878 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch145
2025-09-01 13:02:02.031 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 145/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, 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: 1.728e-03, size: 256, ETA: 2:36:56
2025-09-01 13:02:05.451 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 145/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.169s, data_time: 0.003s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.728e-03, size: 544, ETA: 2:36:53
2025-09-01 13:02:08.723 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 145/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.1, lr: 1.727e-03, size: 352, ETA: 2:36:50
2025-09-01 13:02:11.884 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 145/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.156s, 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: 1.727e-03, size: 288, ETA: 2:36:47
2025-09-01 13:02:15.239 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 145/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.166s, 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: 1.726e-03, size: 256, ETA: 2:36:44
2025-09-01 13:02:18.503 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 145/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.726e-03, size: 544, ETA: 2:36:41
2025-09-01 13:02:19.967 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:02:26.117 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:02:29.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:02:31.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5177
2025-09-01 13:02:31.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4811
2025-09-01 13:02:31.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2864
2025-09-01 13:02:31.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4284
2025-09-01 13:02:31.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:02:31.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:02:31.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.518
2025-09-01 13:02:31.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-09-01 13:02:31.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.286
2025-09-01 13:02:31.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.428
2025-09-01 13:02:31.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:02:31.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:02:31.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:02:31.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:02:31.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:02:31.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:02:31.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:02:31.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:02:31.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:02:34.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:02:36.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:02:39.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:02:41.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:02:44.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:02:46.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:02:49.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:02:51.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:02:54.510 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:02:54.510 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 13:02:54.511 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 13:02:54.511 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:02:54.539 | 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

2025-09-01 13:02:54.541 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:02:54.628 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:02:54.719 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch146
2025-09-01 13:02:57.877 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 146/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.725e-03, size: 320, ETA: 2:36:36
2025-09-01 13:03:01.182 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 146/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.724e-03, size: 256, ETA: 2:36:33
2025-09-01 13:03:04.347 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 146/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.724e-03, size: 352, ETA: 2:36:29
2025-09-01 13:03:07.638 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 146/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.162s, 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: 1.723e-03, size: 288, ETA: 2:36:26
2025-09-01 13:03:10.903 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 146/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.723e-03, size: 544, ETA: 2:36:23
2025-09-01 13:03:14.389 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 146/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.171s, 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: 1.722e-03, size: 544, ETA: 2:36:20
2025-09-01 13:03:15.898 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:03:22.147 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:03:24.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:03:26.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5564
2025-09-01 13:03:27.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4967
2025-09-01 13:03:27.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2948
2025-09-01 13:03:27.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4493
2025-09-01 13:03:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:03:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:03:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.556
2025-09-01 13:03:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-09-01 13:03:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-09-01 13:03:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.449
2025-09-01 13:03:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:03:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:03:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:03:27.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:03:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:03:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:03:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:03:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:03:27.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:03:29.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:03:31.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:03:33.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:03:35.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:03:38.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:03:40.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:03:42.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:03:44.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:03:46.900 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:03:46.900 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 13:03:46.900 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:03:46.900 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:03:46.926 | 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: 7.23 ms

2025-09-01 13:03:46.927 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:03:47.008 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:03:47.096 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch147
2025-09-01 13:03:50.282 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 147/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.7, lr: 1.721e-03, size: 512, ETA: 2:36:16
2025-09-01 13:03:53.572 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 147/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, 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: 1.721e-03, size: 352, ETA: 2:36:13
2025-09-01 13:03:56.649 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 147/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, 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: 0.9, lr: 1.720e-03, size: 256, ETA: 2:36:09
2025-09-01 13:03:59.976 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 147/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, 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: 1.720e-03, size: 512, ETA: 2:36:06
2025-09-01 13:04:03.334 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 147/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, 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: 1.719e-03, size: 384, ETA: 2:36:03
2025-09-01 13:04:06.552 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 147/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.718e-03, size: 512, ETA: 2:36:00
2025-09-01 13:04:08.074 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:04:14.382 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:04:16.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:04:17.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5416
2025-09-01 13:04:17.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4786
2025-09-01 13:04:17.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3239
2025-09-01 13:04:17.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4480
2025-09-01 13:04:17.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:04:17.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:04:17.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-09-01 13:04:17.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-09-01 13:04:17.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.324
2025-09-01 13:04:17.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.448
2025-09-01 13:04:17.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:04:17.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:04:17.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:04:17.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:04:17.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:04:17.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:04:17.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:04:17.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:04:17.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:04:19.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:04:21.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:04:22.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:04:24.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:04:25.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:04:27.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:04:28.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:04:30.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:04:31.990 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:04:31.991 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:04:31.991 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:04:31.991 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:04:32.016 | 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

2025-09-01 13:04:32.025 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:04:32.144 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:04:32.242 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch148
2025-09-01 13:04:35.533 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 148/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.718e-03, size: 448, ETA: 2:35:55
2025-09-01 13:04:38.735 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 148/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 6.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.717e-03, size: 384, ETA: 2:35:52
2025-09-01 13:04:41.960 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 148/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.716e-03, size: 256, ETA: 2:35:49
2025-09-01 13:04:45.270 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 148/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.716e-03, size: 448, ETA: 2:35:46
2025-09-01 13:04:48.459 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 148/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.715e-03, size: 480, ETA: 2:35:43
2025-09-01 13:04:51.820 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 148/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.166s, 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: 1.715e-03, size: 384, ETA: 2:35:40
2025-09-01 13:04:53.257 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:04:59.487 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:05:01.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:05:02.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5505
2025-09-01 13:05:03.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4722
2025-09-01 13:05:03.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3130
2025-09-01 13:05:03.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4452
2025-09-01 13:05:03.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:05:03.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:05:03.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.550
2025-09-01 13:05:03.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-09-01 13:05:03.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.313
2025-09-01 13:05:03.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.445
2025-09-01 13:05:03.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:05:03.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:05:03.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:05:03.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:05:03.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:05:03.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:05:03.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:05:03.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:05:03.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:05:04.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:05:06.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:05:08.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:05:09.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:05:11.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:05:13.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:05:14.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:05:16.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:05:18.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:05:18.027 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:05:18.027 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:05:18.027 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:05:18.058 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.37 ms, Average NMS time: 0.94 ms, Average inference time: 7.30 ms

2025-09-01 13:05:18.060 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:05:18.144 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:05:18.231 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch149
2025-09-01 13:05:21.377 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 149/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.156s, 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: 1.714e-03, size: 256, ETA: 2:35:35
2025-09-01 13:05:24.568 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 149/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.157s, 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: 1.713e-03, size: 384, ETA: 2:35:31
2025-09-01 13:05:27.783 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 149/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.713e-03, size: 352, ETA: 2:35:28
2025-09-01 13:05:31.109 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 149/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.712e-03, size: 352, ETA: 2:35:25
2025-09-01 13:05:34.398 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 149/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.712e-03, size: 416, ETA: 2:35:22
2025-09-01 13:05:37.502 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 149/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.9, lr: 1.711e-03, size: 384, ETA: 2:35:18
2025-09-01 13:05:38.971 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:05:45.408 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:05:47.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:05:49.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5373
2025-09-01 13:05:49.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4660
2025-09-01 13:05:49.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3076
2025-09-01 13:05:49.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4370
2025-09-01 13:05:49.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:05:49.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:05:49.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-09-01 13:05:49.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-09-01 13:05:49.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.308
2025-09-01 13:05:49.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.437
2025-09-01 13:05:49.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:05:49.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:05:49.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:05:49.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:05:49.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:05:49.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:05:49.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:05:49.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:05:49.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:05:51.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:05:53.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:05:55.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:05:57.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:05:59.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:06:01.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:06:03.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:06:05.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:06:07.560 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:06:07.560 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-09-01 13:06:07.560 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:06:07.561 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:06:07.587 | 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

2025-09-01 13:06:07.588 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:06:07.663 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:06:07.791 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch150
2025-09-01 13:06:11.068 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 150/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.162s, 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: 1.710e-03, size: 256, ETA: 2:35:14
2025-09-01 13:06:14.271 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 150/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 7.5, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.710e-03, size: 480, ETA: 2:35:10
2025-09-01 13:06:17.494 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 150/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.159s, 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: 1.709e-03, size: 544, ETA: 2:35:07
2025-09-01 13:06:20.868 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 150/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.166s, 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: 1.709e-03, size: 544, ETA: 2:35:04
2025-09-01 13:06:24.112 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 150/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.1, lr: 1.708e-03, size: 256, ETA: 2:35:01
2025-09-01 13:06:27.326 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 150/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.707e-03, size: 480, ETA: 2:34:58
2025-09-01 13:06:28.804 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:06:35.112 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:06:37.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:06:38.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5719
2025-09-01 13:06:39.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4793
2025-09-01 13:06:39.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2638
2025-09-01 13:06:39.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4383
2025-09-01 13:06:39.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:06:39.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:06:39.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-09-01 13:06:39.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-09-01 13:06:39.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.264
2025-09-01 13:06:39.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.438
2025-09-01 13:06:39.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:06:39.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:06:39.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:06:39.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:06:39.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:06:39.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:06:39.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:06:39.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:06:39.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:06:40.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:06:42.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:06:44.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:06:46.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:06:48.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:06:50.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:06:51.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:06:53.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:06:55.342 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:06:55.343 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:06:55.343 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:06:55.343 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:06:55.368 | 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

2025-09-01 13:06:55.369 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:06:55.460 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:06:55.551 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch151
2025-09-01 13:06:58.783 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 151/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.707e-03, size: 288, ETA: 2:34:53
2025-09-01 13:07:02.017 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 151/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.706e-03, size: 480, ETA: 2:34:50
2025-09-01 13:07:05.209 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 151/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.2, lr: 1.705e-03, size: 288, ETA: 2:34:47
2025-09-01 13:07:08.423 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 151/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.9, lr: 1.705e-03, size: 416, ETA: 2:34:43
2025-09-01 13:07:11.889 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 151/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.171s, 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: 1.704e-03, size: 448, ETA: 2:34:41
2025-09-01 13:07:15.128 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 151/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.160s, 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: 1.704e-03, size: 448, ETA: 2:34:38
2025-09-01 13:07:16.503 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:07:22.683 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:07:24.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:07:26.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5374
2025-09-01 13:07:26.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4979
2025-09-01 13:07:26.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2712
2025-09-01 13:07:26.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4355
2025-09-01 13:07:26.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:07:26.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:07:26.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-09-01 13:07:26.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-09-01 13:07:26.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.271
2025-09-01 13:07:26.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.435
2025-09-01 13:07:26.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:07:26.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:07:26.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:07:26.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:07:26.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:07:26.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:07:26.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:07:26.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:07:26.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:07:28.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:07:30.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:07:32.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:07:34.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:07:36.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:07:38.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:07:39.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:07:41.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:07:43.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:07:43.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:07:43.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:07:43.631 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:07:43.658 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 1.00 ms, Average inference time: 7.16 ms

2025-09-01 13:07:43.659 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:07:43.749 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:07:43.887 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch152
2025-09-01 13:07:47.005 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 152/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.703e-03, size: 480, ETA: 2:34:32
2025-09-01 13:07:50.316 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 152/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.702e-03, size: 448, ETA: 2:34:29
2025-09-01 13:07:53.763 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 152/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.170s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.702e-03, size: 288, ETA: 2:34:27
2025-09-01 13:07:57.050 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 152/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.701e-03, size: 544, ETA: 2:34:24
2025-09-01 13:08:00.434 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 152/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.166s, 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: 1.701e-03, size: 544, ETA: 2:34:21
2025-09-01 13:08:03.656 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 152/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.700e-03, size: 544, ETA: 2:34:17
2025-09-01 13:08:05.165 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:08:11.584 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:08:14.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:08:17.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5449
2025-09-01 13:08:17.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4784
2025-09-01 13:08:17.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2747
2025-09-01 13:08:17.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4327
2025-09-01 13:08:17.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:08:17.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:08:17.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-09-01 13:08:17.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.478
2025-09-01 13:08:17.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-09-01 13:08:17.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.433
2025-09-01 13:08:17.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:08:17.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:08:17.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:08:17.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:08:17.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:08:17.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:08:17.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:08:17.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:08:17.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:08:20.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:08:22.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:08:25.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:08:28.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:08:30.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:08:33.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:08:36.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:08:38.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:08:41.510 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:08:41.511 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 13:08:41.511 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 13:08:41.511 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:08:41.538 | 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

2025-09-01 13:08:41.539 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:08:41.621 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:08:41.762 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch153
2025-09-01 13:08:45.073 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 153/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.2, lr: 1.699e-03, size: 288, ETA: 2:34:13
2025-09-01 13:08:48.220 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 153/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, 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: 0.8, lr: 1.699e-03, size: 416, ETA: 2:34:10
2025-09-01 13:08:51.565 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 153/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.165s, 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: 1.698e-03, size: 480, ETA: 2:34:07
2025-09-01 13:08:54.856 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 153/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, 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: 0.8, lr: 1.697e-03, size: 512, ETA: 2:34:04
2025-09-01 13:08:58.187 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 153/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.165s, 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: 1.697e-03, size: 352, ETA: 2:34:01
2025-09-01 13:09:01.324 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 153/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.696e-03, size: 416, ETA: 2:33:57
2025-09-01 13:09:02.802 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:09:09.234 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:09:13.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:09:17.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5134
2025-09-01 13:09:18.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4798
2025-09-01 13:09:18.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2914
2025-09-01 13:09:18.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4282
2025-09-01 13:09:18.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:09:18.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:09:18.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-09-01 13:09:18.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.480
2025-09-01 13:09:18.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.291
2025-09-01 13:09:18.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.428
2025-09-01 13:09:18.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:09:18.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:09:18.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:09:18.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:09:18.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:09:18.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:09:18.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:09:18.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:09:18.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:09:22.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:09:26.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:09:30.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:09:34.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:09:38.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:09:42.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:09:46.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:09:50.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:09:54.578 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:09:54.578 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:09:54.578 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 13:09:54.578 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:09:54.607 | 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

2025-09-01 13:09:54.608 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:09:54.745 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:09:54.832 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch154
2025-09-01 13:09:57.834 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 154/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.695e-03, size: 512, ETA: 2:33:52
2025-09-01 13:10:01.144 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 154/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.0, lr: 1.695e-03, size: 416, ETA: 2:33:49
2025-09-01 13:10:04.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 154/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.164s, 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: 1.694e-03, size: 384, ETA: 2:33:46
2025-09-01 13:10:07.654 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 154/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.694e-03, size: 544, ETA: 2:33:43
2025-09-01 13:10:10.934 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 154/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.693e-03, size: 544, ETA: 2:33:40
2025-09-01 13:10:14.187 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 154/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.692e-03, size: 512, ETA: 2:33:36
2025-09-01 13:10:15.715 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:10:22.019 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:10:24.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:10:25.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5612
2025-09-01 13:10:26.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4844
2025-09-01 13:10:26.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2996
2025-09-01 13:10:26.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4484
2025-09-01 13:10:26.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:10:26.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:10:26.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.561
2025-09-01 13:10:26.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-09-01 13:10:26.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.300
2025-09-01 13:10:26.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.448
2025-09-01 13:10:26.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:10:26.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:10:26.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:10:26.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:10:26.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:10:26.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:10:26.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:10:26.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:10:26.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:10:28.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:10:30.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:10:31.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:10:33.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:10:35.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:10:37.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:10:39.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:10:41.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:10:43.410 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:10:43.410 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 13:10:43.410 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:10:43.410 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:10:43.439 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.37 ms, Average NMS time: 0.97 ms, Average inference time: 7.33 ms

2025-09-01 13:10:43.440 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:10:43.524 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:10:43.610 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch155
2025-09-01 13:10:46.718 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 155/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.2, lr: 1.692e-03, size: 544, ETA: 2:33:32
2025-09-01 13:10:50.038 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 155/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.691e-03, size: 384, ETA: 2:33:29
2025-09-01 13:10:53.190 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 155/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 1.1, lr: 1.690e-03, size: 384, ETA: 2:33:25
2025-09-01 13:10:56.364 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 155/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.1, lr: 1.690e-03, size: 416, ETA: 2:33:22
2025-09-01 13:10:59.675 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 155/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.163s, 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: 1.689e-03, size: 544, ETA: 2:33:19
2025-09-01 13:11:02.837 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 155/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.1Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.689e-03, size: 416, ETA: 2:33:15
2025-09-01 13:11:04.331 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:11:10.632 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:11:12.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:11:14.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5586
2025-09-01 13:11:14.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4914
2025-09-01 13:11:14.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3011
2025-09-01 13:11:14.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4503
2025-09-01 13:11:14.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:11:14.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:11:14.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-09-01 13:11:14.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-09-01 13:11:14.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.301
2025-09-01 13:11:14.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.450
2025-09-01 13:11:14.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:11:14.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:11:14.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:11:14.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:11:14.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:11:14.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:11:14.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:11:14.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:11:14.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:11:16.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:11:17.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:11:19.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:11:21.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:11:22.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:11:24.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:11:26.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:11:27.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:11:29.522 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:11:29.522 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:11:29.522 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:11:29.522 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:11:29.548 | 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

2025-09-01 13:11:29.550 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:11:29.692 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:11:29.807 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch156
2025-09-01 13:11:32.938 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 156/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.688e-03, size: 480, ETA: 2:33:10
2025-09-01 13:11:36.313 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 156/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.166s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.687e-03, size: 480, ETA: 2:33:07
2025-09-01 13:11:39.452 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 156/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.687e-03, size: 416, ETA: 2:33:04
2025-09-01 13:11:42.712 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 156/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.161s, 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: 1.686e-03, size: 448, ETA: 2:33:01
2025-09-01 13:11:46.077 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 156/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.166s, 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: 1.685e-03, size: 448, ETA: 2:32:58
2025-09-01 13:11:49.439 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 156/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.685e-03, size: 320, ETA: 2:32:55
2025-09-01 13:11:50.945 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:11:57.348 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:11:59.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:12:01.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5766
2025-09-01 13:12:01.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4958
2025-09-01 13:12:01.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2918
2025-09-01 13:12:01.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4547
2025-09-01 13:12:01.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:12:01.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:12:01.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.577
2025-09-01 13:12:01.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-09-01 13:12:01.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.292
2025-09-01 13:12:01.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.455
2025-09-01 13:12:01.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:12:01.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:12:01.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:12:01.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:12:01.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:12:01.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:12:01.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:12:01.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:12:01.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:12:04.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:12:06.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:12:08.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:12:10.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:12:12.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:12:14.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:12:16.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:12:18.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:12:20.984 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:12:20.984 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 13:12:20.985 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:12:20.985 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:12:21.012 | 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

2025-09-01 13:12:21.016 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:12:21.194 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:12:21.281 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch157
2025-09-01 13:12:24.543 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 157/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.684e-03, size: 480, ETA: 2:32:51
2025-09-01 13:12:27.744 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 157/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.157s, 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: 1.683e-03, size: 352, ETA: 2:32:47
2025-09-01 13:12:31.083 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 157/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.6, lr: 1.683e-03, size: 256, ETA: 2:32:44
2025-09-01 13:12:34.369 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 157/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.682e-03, size: 384, ETA: 2:32:41
2025-09-01 13:12:37.626 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 157/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.162s, 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: 1.682e-03, size: 288, ETA: 2:32:38
2025-09-01 13:12:40.859 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 157/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.160s, 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: 1.681e-03, size: 512, ETA: 2:32:35
2025-09-01 13:12:42.374 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:12:48.514 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:12:51.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:12:53.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5312
2025-09-01 13:12:54.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4854
2025-09-01 13:12:54.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2968
2025-09-01 13:12:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4378
2025-09-01 13:12:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:12:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:12:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-09-01 13:12:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-09-01 13:12:54.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.297
2025-09-01 13:12:54.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.438
2025-09-01 13:12:54.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:12:54.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:12:54.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:12:54.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:12:54.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:12:54.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:12:54.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:12:54.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:12:54.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:12:56.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:12:59.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:13:01.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:13:04.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:13:06.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:13:09.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:13:11.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:13:14.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:13:17.054 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:13:17.055 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:13:17.055 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:13:17.055 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:13:17.083 | 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

2025-09-01 13:13:17.084 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:13:17.223 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:13:17.314 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch158
2025-09-01 13:13:20.340 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 158/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.680e-03, size: 384, ETA: 2:32:30
2025-09-01 13:13:23.577 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 158/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, 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: 1.680e-03, size: 320, ETA: 2:32:27
2025-09-01 13:13:26.724 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 158/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, 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: 0.9, lr: 1.679e-03, size: 416, ETA: 2:32:23
2025-09-01 13:13:30.000 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 158/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.162s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.678e-03, size: 512, ETA: 2:32:20
2025-09-01 13:13:33.391 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 158/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.167s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.9, lr: 1.678e-03, size: 576, ETA: 2:32:17
2025-09-01 13:13:36.855 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 158/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.171s, 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: 1.677e-03, size: 480, ETA: 2:32:15
2025-09-01 13:13:38.329 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:13:44.652 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:13:46.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:13:48.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5569
2025-09-01 13:13:48.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5120
2025-09-01 13:13:48.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3132
2025-09-01 13:13:48.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4607
2025-09-01 13:13:48.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:13:48.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:13:48.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.557
2025-09-01 13:13:48.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-09-01 13:13:48.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.313
2025-09-01 13:13:48.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.461
2025-09-01 13:13:48.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:13:48.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:13:48.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:13:48.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:13:48.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:13:48.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:13:48.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:13:48.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:13:48.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:13:50.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:13:52.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:13:54.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:13:56.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:13:58.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:14:00.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:14:01.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:14:03.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:14:05.314 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:14:05.314 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 13:14:05.314 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 13:14:05.315 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:14:05.340 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.97 ms, Average inference time: 7.25 ms

2025-09-01 13:14:05.342 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:14:05.479 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:14:05.571 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch159
2025-09-01 13:14:08.677 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 159/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.9, lr: 1.676e-03, size: 288, ETA: 2:32:09
2025-09-01 13:14:11.951 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 159/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.162s, 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: 1.676e-03, size: 384, ETA: 2:32:06
2025-09-01 13:14:15.179 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 159/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, 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: 1.0, lr: 1.675e-03, size: 544, ETA: 2:32:03
2025-09-01 13:14:18.528 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 159/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.166s, 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: 1.674e-03, size: 416, ETA: 2:32:00
2025-09-01 13:14:21.818 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 159/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.162s, 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: 1.674e-03, size: 512, ETA: 2:31:57
2025-09-01 13:14:25.011 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 159/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 7.9, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.673e-03, size: 352, ETA: 2:31:54
2025-09-01 13:14:26.419 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:14:32.640 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:14:36.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:14:39.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5511
2025-09-01 13:14:40.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4751
2025-09-01 13:14:40.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2871
2025-09-01 13:14:40.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4378
2025-09-01 13:14:40.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:14:40.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:14:40.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-09-01 13:14:40.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.475
2025-09-01 13:14:40.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.287
2025-09-01 13:14:40.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.438
2025-09-01 13:14:40.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:14:40.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:14:40.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:14:40.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:14:40.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:14:40.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:14:40.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:14:40.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:14:40.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:14:43.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:14:47.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:14:50.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:14:53.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:14:57.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:15:00.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:15:04.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:15:07.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:15:10.757 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:15:10.758 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:15:10.758 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:15:10.759 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:15:10.789 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 1.01 ms, Average inference time: 7.23 ms

2025-09-01 13:15:10.791 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:15:10.874 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:15:10.961 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch160
2025-09-01 13:15:14.068 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 160/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.4, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.672e-03, size: 352, ETA: 2:31:49
2025-09-01 13:15:17.359 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 160/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 7.6, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.672e-03, size: 352, ETA: 2:31:46
2025-09-01 13:15:20.608 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 160/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.160s, 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: 1.671e-03, size: 320, ETA: 2:31:42
2025-09-01 13:15:23.858 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 160/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.671e-03, size: 544, ETA: 2:31:39
2025-09-01 13:15:27.184 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 160/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.670e-03, size: 416, ETA: 2:31:36
2025-09-01 13:15:30.417 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 160/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 8.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 1.2, lr: 1.669e-03, size: 448, ETA: 2:31:33
2025-09-01 13:15:32.004 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:15:38.262 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:15:41.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:15:43.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5716
2025-09-01 13:15:44.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5245
2025-09-01 13:15:44.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2914
2025-09-01 13:15:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4625
2025-09-01 13:15:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:15:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:15:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-09-01 13:15:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-09-01 13:15:44.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.291
2025-09-01 13:15:44.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.462
2025-09-01 13:15:44.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:15:44.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:15:44.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:15:44.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:15:44.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:15:44.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:15:44.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:15:44.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:15:44.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:15:46.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:15:49.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:15:52.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:15:54.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:15:57.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:16:00.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:16:02.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:16:05.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:16:07.940 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:16:07.940 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 13:16:07.940 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 13:16:07.940 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:16:07.966 | 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

2025-09-01 13:16:07.967 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:16:08.053 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:16:08.139 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch161
2025-09-01 13:16:11.186 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 161/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.151s, 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: 1.669e-03, size: 288, ETA: 2:31:28
2025-09-01 13:16:14.376 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 161/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 7.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.668e-03, size: 480, ETA: 2:31:25
2025-09-01 13:16:17.637 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 161/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.161s, 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: 1.667e-03, size: 544, ETA: 2:31:21
2025-09-01 13:16:20.896 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 161/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.160s, 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: 1.667e-03, size: 512, ETA: 2:31:18
2025-09-01 13:16:24.070 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 161/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.157s, 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: 1.666e-03, size: 352, ETA: 2:31:15
2025-09-01 13:16:27.349 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 161/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.666e-03, size: 576, ETA: 2:31:12
2025-09-01 13:16:28.920 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:16:35.180 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:16:36.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:16:37.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5456
2025-09-01 13:16:37.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4813
2025-09-01 13:16:37.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2862
2025-09-01 13:16:37.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4377
2025-09-01 13:16:37.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:16:37.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:16:37.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.546
2025-09-01 13:16:37.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-09-01 13:16:37.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.286
2025-09-01 13:16:37.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.438
2025-09-01 13:16:37.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:16:37.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:16:37.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:16:37.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:16:37.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:16:37.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:16:37.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:16:37.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:16:37.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:16:39.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:16:40.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:16:41.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:16:42.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:16:44.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:16:45.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:16:46.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:16:47.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:16:49.037 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:16:49.037 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 13:16:49.037 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:16:49.037 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:16:49.045 | 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

2025-09-01 13:16:49.052 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:16:49.158 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:16:49.318 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch162
2025-09-01 13:16:52.490 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 162/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.665e-03, size: 512, ETA: 2:31:07
2025-09-01 13:16:55.819 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 162/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.664e-03, size: 480, ETA: 2:31:04
2025-09-01 13:16:58.910 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 162/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.153s, 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: 1.663e-03, size: 352, ETA: 2:31:01
2025-09-01 13:17:02.207 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 162/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.8, lr: 1.663e-03, size: 416, ETA: 2:30:58
2025-09-01 13:17:05.405 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 162/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.1, lr: 1.662e-03, size: 384, ETA: 2:30:54
2025-09-01 13:17:08.816 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 162/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.662e-03, size: 576, ETA: 2:30:52
2025-09-01 13:17:10.310 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:17:16.530 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:17:18.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:17:20.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5603
2025-09-01 13:17:20.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5066
2025-09-01 13:17:20.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2571
2025-09-01 13:17:20.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4413
2025-09-01 13:17:20.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:17:20.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:17:20.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.560
2025-09-01 13:17:20.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-09-01 13:17:20.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.257
2025-09-01 13:17:20.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.441
2025-09-01 13:17:20.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:17:20.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:17:20.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:17:20.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:17:20.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:17:20.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:17:20.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:17:20.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:17:20.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:17:22.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:17:24.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:17:26.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:17:28.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:17:30.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:17:32.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:17:34.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:17:36.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:17:38.413 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:17:38.413 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:17:38.413 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:17:38.413 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:17:38.440 | 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

2025-09-01 13:17:38.442 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:17:38.528 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:17:38.618 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch163
2025-09-01 13:17:41.764 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 163/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.661e-03, size: 320, ETA: 2:30:47
2025-09-01 13:17:45.071 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 163/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.162s, data_time: 0.003s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.2, lr: 1.660e-03, size: 288, ETA: 2:30:44
2025-09-01 13:17:48.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 163/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.659e-03, size: 576, ETA: 2:30:40
2025-09-01 13:17:51.533 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 163/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, 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: 0.8, lr: 1.659e-03, size: 480, ETA: 2:30:37
2025-09-01 13:17:54.733 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 163/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.658e-03, size: 256, ETA: 2:30:34
2025-09-01 13:17:58.156 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 163/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.169s, 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: 1.658e-03, size: 480, ETA: 2:30:31
2025-09-01 13:17:59.554 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:18:05.833 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:18:09.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:18:11.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5347
2025-09-01 13:18:12.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4464
2025-09-01 13:18:12.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3033
2025-09-01 13:18:12.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4281
2025-09-01 13:18:12.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:18:12.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:18:12.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-09-01 13:18:12.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.446
2025-09-01 13:18:12.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.303
2025-09-01 13:18:12.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.428
2025-09-01 13:18:12.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:18:12.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:18:12.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:18:12.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:18:12.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:18:12.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:18:12.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:18:12.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:18:12.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:18:15.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:18:18.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:18:21.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:18:24.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:18:26.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:18:29.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:18:32.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:18:35.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:18:38.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:18:38.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:18:38.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 13:18:38.630 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:18:38.658 | 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

2025-09-01 13:18:38.659 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:18:38.789 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:18:38.877 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch164
2025-09-01 13:18:42.089 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 164/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.159s, 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: 1.657e-03, size: 352, ETA: 2:30:26
2025-09-01 13:18:45.434 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 164/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.3, lr: 1.656e-03, size: 576, ETA: 2:30:23
2025-09-01 13:18:48.799 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 164/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.166s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.656e-03, size: 416, ETA: 2:30:20
2025-09-01 13:18:52.140 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 164/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.655e-03, size: 384, ETA: 2:30:17
2025-09-01 13:18:55.339 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 164/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.654e-03, size: 480, ETA: 2:30:14
2025-09-01 13:18:58.742 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 164/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.168s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.0, lr: 1.654e-03, size: 448, ETA: 2:30:11
2025-09-01 13:19:00.316 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:19:06.485 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:19:08.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:19:10.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5601
2025-09-01 13:19:10.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4994
2025-09-01 13:19:11.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3111
2025-09-01 13:19:11.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4569
2025-09-01 13:19:11.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:19:11.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:19:11.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.560
2025-09-01 13:19:11.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-09-01 13:19:11.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.311
2025-09-01 13:19:11.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.457
2025-09-01 13:19:11.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:19:11.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:19:11.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:19:11.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:19:11.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:19:11.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:19:11.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:19:11.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:19:11.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:19:13.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:19:15.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:19:17.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:19:19.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:19:21.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:19:23.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:19:25.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:19:27.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:19:29.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:19:29.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 13:19:29.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 13:19:29.861 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:19:29.889 | 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

2025-09-01 13:19:29.890 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:19:29.971 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:19:30.112 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch165
2025-09-01 13:19:33.259 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 165/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.1, lr: 1.653e-03, size: 288, ETA: 2:30:07
2025-09-01 13:19:36.463 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 165/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.157s, 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: 1.652e-03, size: 320, ETA: 2:30:03
2025-09-01 13:19:39.746 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 165/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 8.5, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.3, cls_loss: 1.0, lr: 1.652e-03, size: 448, ETA: 2:30:00
2025-09-01 13:19:43.004 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 165/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.160s, 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: 1.651e-03, size: 288, ETA: 2:29:57
2025-09-01 13:19:46.115 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 165/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.153s, 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: 1.650e-03, size: 256, ETA: 2:29:53
2025-09-01 13:19:49.385 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 165/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.650e-03, size: 480, ETA: 2:29:50
2025-09-01 13:19:50.909 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:19:57.150 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:20:02.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:20:06.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5171
2025-09-01 13:20:07.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4826
2025-09-01 13:20:07.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2745
2025-09-01 13:20:07.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4247
2025-09-01 13:20:07.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:20:07.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:20:07.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 13:20:07.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.483
2025-09-01 13:20:07.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.274
2025-09-01 13:20:07.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.425
2025-09-01 13:20:07.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:20:07.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:20:07.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:20:07.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:20:07.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:20:07.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:20:07.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:20:07.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:20:07.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:20:11.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:20:16.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:20:20.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:20:25.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:20:30.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:20:34.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:20:39.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:20:43.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:20:48.326 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:20:48.326 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:20:48.327 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-09-01 13:20:48.327 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:20:48.353 | 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

2025-09-01 13:20:48.354 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:20:48.437 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:20:48.527 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch166
2025-09-01 13:20:51.746 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 166/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.159s, 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: 1.649e-03, size: 544, ETA: 2:29:46
2025-09-01 13:20:55.153 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 166/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.168s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.648e-03, size: 480, ETA: 2:29:43
2025-09-01 13:20:58.462 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 166/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.163s, 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: 1.648e-03, size: 320, ETA: 2:29:40
2025-09-01 13:21:01.742 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 166/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.647e-03, size: 416, ETA: 2:29:37
2025-09-01 13:21:05.108 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 166/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.166s, 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: 1.646e-03, size: 576, ETA: 2:29:34
2025-09-01 13:21:08.542 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 166/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.646e-03, size: 480, ETA: 2:29:31
2025-09-01 13:21:10.016 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:21:16.324 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:21:18.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:21:20.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5582
2025-09-01 13:21:20.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5008
2025-09-01 13:21:20.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2946
2025-09-01 13:21:20.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4512
2025-09-01 13:21:20.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:21:20.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:21:20.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.558
2025-09-01 13:21:20.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-09-01 13:21:20.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-09-01 13:21:20.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.451
2025-09-01 13:21:20.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:21:20.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:21:20.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:21:20.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:21:20.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:21:20.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:21:20.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:21:20.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:21:20.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:21:22.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:21:24.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:21:26.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:21:28.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:21:29.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:21:31.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:21:33.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:21:35.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:21:37.446 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:21:37.446 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 13:21:37.446 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:21:37.446 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:21:37.472 | 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

2025-09-01 13:21:37.474 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:21:37.556 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:21:37.645 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch167
2025-09-01 13:21:40.859 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 167/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.157s, 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: 1.645e-03, size: 512, ETA: 2:29:26
2025-09-01 13:21:44.205 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 167/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.165s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.644e-03, size: 320, ETA: 2:29:23
2025-09-01 13:21:47.528 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 167/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, 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: 1.0, lr: 1.644e-03, size: 416, ETA: 2:29:20
2025-09-01 13:21:50.736 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 167/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.158s, 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: 1.643e-03, size: 256, ETA: 2:29:17
2025-09-01 13:21:53.988 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 167/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.642e-03, size: 480, ETA: 2:29:14
2025-09-01 13:21:57.382 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 167/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.168s, 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: 1.642e-03, size: 480, ETA: 2:29:11
2025-09-01 13:21:58.915 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:22:05.283 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:22:08.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:22:09.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5592
2025-09-01 13:22:10.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4947
2025-09-01 13:22:10.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3122
2025-09-01 13:22:10.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4554
2025-09-01 13:22:10.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:22:10.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:22:10.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-09-01 13:22:10.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-09-01 13:22:10.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-09-01 13:22:10.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.455
2025-09-01 13:22:10.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:22:10.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:22:10.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:22:10.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:22:10.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:22:10.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:22:10.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:22:10.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:22:10.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:22:12.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:22:14.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:22:17.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:22:19.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:22:21.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:22:23.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:22:26.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:22:28.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:22:30.573 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:22:30.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:22:30.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 13:22:30.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:22:30.603 | 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: 7.10 ms

2025-09-01 13:22:30.604 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:22:30.735 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:22:30.824 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch168
2025-09-01 13:22:33.875 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 168/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.150s, 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: 1.641e-03, size: 352, ETA: 2:29:06
2025-09-01 13:22:37.232 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 168/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.640e-03, size: 288, ETA: 2:29:03
2025-09-01 13:22:40.574 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 168/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.9, lr: 1.640e-03, size: 288, ETA: 2:29:00
2025-09-01 13:22:43.759 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 168/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.157s, 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: 1.639e-03, size: 416, ETA: 2:28:57
2025-09-01 13:22:46.885 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 168/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.155s, 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: 1.638e-03, size: 416, ETA: 2:28:53
2025-09-01 13:22:50.080 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 168/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.158s, 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: 1.638e-03, size: 384, ETA: 2:28:50
2025-09-01 13:22:51.563 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:22:57.915 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:22:59.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:23:00.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5640
2025-09-01 13:23:00.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5023
2025-09-01 13:23:01.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2686
2025-09-01 13:23:01.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4450
2025-09-01 13:23:01.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:23:01.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:23:01.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.564
2025-09-01 13:23:01.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-09-01 13:23:01.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.269
2025-09-01 13:23:01.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.445
2025-09-01 13:23:01.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:23:01.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:23:01.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:23:01.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:23:01.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:23:01.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:23:01.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:23:01.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:23:01.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:23:02.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:23:03.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:23:05.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:23:06.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:23:07.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:23:09.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:23:10.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:23:11.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:23:13.314 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:23:13.314 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:23:13.314 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:23:13.314 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:23:13.324 | 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: 7.26 ms

2025-09-01 13:23:13.325 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:23:13.492 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:23:13.579 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch169
2025-09-01 13:23:16.766 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 169/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, 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: 0.9, lr: 1.637e-03, size: 320, ETA: 2:28:45
2025-09-01 13:23:19.946 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 169/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.157s, 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: 1.636e-03, size: 448, ETA: 2:28:42
2025-09-01 13:23:23.218 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 169/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.636e-03, size: 480, ETA: 2:28:39
2025-09-01 13:23:26.463 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 169/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.161s, 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: 1.635e-03, size: 448, ETA: 2:28:35
2025-09-01 13:23:29.605 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 169/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.155s, 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: 1.634e-03, size: 320, ETA: 2:28:32
2025-09-01 13:23:32.758 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 169/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.155s, 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: 1.634e-03, size: 576, ETA: 2:28:28
2025-09-01 13:23:34.323 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:23:40.909 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:23:43.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:23:45.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5283
2025-09-01 13:23:46.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4867
2025-09-01 13:23:46.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2547
2025-09-01 13:23:46.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4232
2025-09-01 13:23:46.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:23:46.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:23:46.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-09-01 13:23:46.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-09-01 13:23:46.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.255
2025-09-01 13:23:46.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.423
2025-09-01 13:23:46.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:23:46.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:23:46.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:23:46.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:23:46.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:23:46.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:23:46.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:23:46.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:23:46.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:23:48.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:23:51.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:23:53.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:23:56.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:23:58.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:24:00.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:24:03.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:24:05.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:24:08.179 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:24:08.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.17
2025-09-01 13:24:08.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-09-01 13:24:08.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:24:08.209 | 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: 7.26 ms

2025-09-01 13:24:08.210 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:24:08.295 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:24:08.442 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch170
2025-09-01 13:24:11.512 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 170/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.633e-03, size: 384, ETA: 2:28:24
2025-09-01 13:24:14.639 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 170/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 5.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.632e-03, size: 448, ETA: 2:28:20
2025-09-01 13:24:17.934 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 170/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.162s, 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: 1.631e-03, size: 256, ETA: 2:28:17
2025-09-01 13:24:21.043 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 170/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.153s, 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: 1.631e-03, size: 352, ETA: 2:28:13
2025-09-01 13:24:24.156 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 170/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 0.9, lr: 1.630e-03, size: 544, ETA: 2:28:10
2025-09-01 13:24:27.469 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 170/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.0, lr: 1.630e-03, size: 320, ETA: 2:28:07
2025-09-01 13:24:28.888 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:24:35.102 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:24:37.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:24:39.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5618
2025-09-01 13:24:40.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4704
2025-09-01 13:24:40.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2778
2025-09-01 13:24:40.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4367
2025-09-01 13:24:40.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:24:40.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:24:40.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.562
2025-09-01 13:24:40.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.470
2025-09-01 13:24:40.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.278
2025-09-01 13:24:40.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.437
2025-09-01 13:24:40.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:24:40.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:24:40.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:24:40.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:24:40.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:24:40.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:24:40.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:24:40.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:24:40.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:24:42.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:24:44.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:24:47.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:24:49.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:24:51.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:24:53.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:24:56.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:24:58.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:25:00.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:25:00.754 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 13:25:00.754 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:25:00.754 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:25:00.780 | 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

2025-09-01 13:25:00.781 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:25:00.865 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:25:00.954 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch171
2025-09-01 13:25:04.082 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 171/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.2, lr: 1.629e-03, size: 288, ETA: 2:28:02
2025-09-01 13:25:07.408 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 171/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.628e-03, size: 256, ETA: 2:27:59
2025-09-01 13:25:10.543 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 171/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.627e-03, size: 288, ETA: 2:27:55
2025-09-01 13:25:13.882 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 171/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.165s, 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: 1.627e-03, size: 352, ETA: 2:27:52
2025-09-01 13:25:17.011 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 171/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.0, lr: 1.626e-03, size: 320, ETA: 2:27:49
2025-09-01 13:25:20.196 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 171/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.626e-03, size: 320, ETA: 2:27:45
2025-09-01 13:25:21.718 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:25:28.104 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:25:30.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:25:31.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5165
2025-09-01 13:25:31.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4705
2025-09-01 13:25:31.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3083
2025-09-01 13:25:31.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4318
2025-09-01 13:25:31.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:25:31.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:25:31.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-09-01 13:25:31.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-09-01 13:25:31.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.308
2025-09-01 13:25:31.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.432
2025-09-01 13:25:31.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:25:31.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:25:31.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:25:31.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:25:31.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:25:31.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:25:31.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:25:31.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:25:31.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:25:33.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:25:35.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:25:36.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:25:38.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:25:40.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:25:41.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:25:43.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:25:45.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:25:46.849 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:25:46.849 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 13:25:46.849 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 13:25:46.849 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:25:46.877 | 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

2025-09-01 13:25:46.878 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:25:46.954 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:25:47.042 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch172
2025-09-01 13:25:50.178 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 172/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.625e-03, size: 384, ETA: 2:27:41
2025-09-01 13:25:53.523 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 172/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.624e-03, size: 416, ETA: 2:27:38
2025-09-01 13:25:56.788 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 172/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.623e-03, size: 544, ETA: 2:27:35
2025-09-01 13:26:00.193 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 172/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.623e-03, size: 576, ETA: 2:27:32
2025-09-01 13:26:03.560 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 172/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.622e-03, size: 448, ETA: 2:27:29
2025-09-01 13:26:06.777 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 172/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.159s, 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: 1.621e-03, size: 288, ETA: 2:27:26
2025-09-01 13:26:08.253 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:26:14.576 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:26:18.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:26:20.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5468
2025-09-01 13:26:21.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4969
2025-09-01 13:26:21.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3012
2025-09-01 13:26:21.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4483
2025-09-01 13:26:21.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:26:21.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:26:21.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.547
2025-09-01 13:26:21.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-09-01 13:26:21.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.301
2025-09-01 13:26:21.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.448
2025-09-01 13:26:21.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:26:21.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:26:21.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:26:21.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:26:21.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:26:21.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:26:21.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:26:21.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:26:21.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:26:24.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:26:27.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:26:30.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:26:33.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:26:36.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:26:39.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:26:42.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:26:45.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:26:48.278 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:26:48.278 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 13:26:48.279 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:26:48.279 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:26:48.305 | 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: 7.23 ms

2025-09-01 13:26:48.306 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:26:48.446 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:26:48.535 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch173
2025-09-01 13:26:51.611 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 173/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.9, lr: 1.621e-03, size: 256, ETA: 2:27:21
2025-09-01 13:26:54.827 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 173/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.158s, 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: 1.620e-03, size: 448, ETA: 2:27:17
2025-09-01 13:26:58.151 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 173/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.619e-03, size: 448, ETA: 2:27:14
2025-09-01 13:27:01.421 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 173/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.8, lr: 1.619e-03, size: 576, ETA: 2:27:11
2025-09-01 13:27:04.807 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 173/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.166s, 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: 1.618e-03, size: 416, ETA: 2:27:08
2025-09-01 13:27:08.007 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 173/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.617e-03, size: 416, ETA: 2:27:05
2025-09-01 13:27:09.390 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:27:15.623 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:27:18.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:27:19.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5303
2025-09-01 13:27:19.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4681
2025-09-01 13:27:20.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3127
2025-09-01 13:27:20.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4370
2025-09-01 13:27:20.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:27:20.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:27:20.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-09-01 13:27:20.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.468
2025-09-01 13:27:20.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.313
2025-09-01 13:27:20.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.437
2025-09-01 13:27:20.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:27:20.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:27:20.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:27:20.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:27:20.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:27:20.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:27:20.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:27:20.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:27:20.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:27:22.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:27:23.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:27:25.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:27:27.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:27:29.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:27:31.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:27:33.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:27:35.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:27:37.541 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:27:37.541 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 13:27:37.541 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:27:37.541 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:27:37.569 | 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

2025-09-01 13:27:37.572 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:27:37.657 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:27:37.748 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch174
2025-09-01 13:27:40.892 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 174/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.155s, 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: 1.616e-03, size: 544, ETA: 2:27:00
2025-09-01 13:27:44.231 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 174/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.165s, data_time: 0.005s, total_loss: 6.4, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 1.0, lr: 1.616e-03, size: 288, ETA: 2:26:57
2025-09-01 13:27:47.438 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 174/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, 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: 1.1, lr: 1.615e-03, size: 288, ETA: 2:26:54
2025-09-01 13:27:50.696 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 174/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.614e-03, size: 544, ETA: 2:26:50
2025-09-01 13:27:53.889 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 174/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.158s, 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: 1.614e-03, size: 352, ETA: 2:26:47
2025-09-01 13:27:57.175 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 174/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.162s, 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: 1.613e-03, size: 352, ETA: 2:26:44
2025-09-01 13:27:58.676 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:28:05.056 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:28:07.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:28:08.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5451
2025-09-01 13:28:08.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4999
2025-09-01 13:28:08.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2898
2025-09-01 13:28:08.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4449
2025-09-01 13:28:08.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:28:08.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:28:08.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-09-01 13:28:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-09-01 13:28:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.290
2025-09-01 13:28:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.445
2025-09-01 13:28:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:28:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:28:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:28:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:28:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:28:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:28:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:28:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:28:08.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:28:10.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:28:11.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:28:13.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:28:15.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:28:16.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:28:18.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:28:19.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:28:21.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:28:22.967 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:28:22.967 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:28:22.968 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:28:22.968 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:28:22.993 | 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

2025-09-01 13:28:22.995 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:28:23.068 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:28:23.152 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch175
2025-09-01 13:28:26.377 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 175/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.158s, 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: 1.612e-03, size: 352, ETA: 2:26:39
2025-09-01 13:28:29.493 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 175/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.612e-03, size: 352, ETA: 2:26:36
2025-09-01 13:28:32.745 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 175/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.611e-03, size: 384, ETA: 2:26:33
2025-09-01 13:28:36.072 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 175/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.164s, 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: 1.610e-03, size: 480, ETA: 2:26:30
2025-09-01 13:28:39.413 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 175/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.610e-03, size: 576, ETA: 2:26:27
2025-09-01 13:28:42.944 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 175/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.174s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 1.0, lr: 1.609e-03, size: 576, ETA: 2:26:24
2025-09-01 13:28:44.459 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:28:50.733 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:28:53.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:28:56.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5449
2025-09-01 13:28:56.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4924
2025-09-01 13:28:56.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2651
2025-09-01 13:28:56.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4341
2025-09-01 13:28:56.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:28:56.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:28:56.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-09-01 13:28:56.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.492
2025-09-01 13:28:56.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.265
2025-09-01 13:28:56.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.434
2025-09-01 13:28:56.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:28:56.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:28:56.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:28:56.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:28:56.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:28:56.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:28:56.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:28:56.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:28:56.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:28:59.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:29:01.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:29:04.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:29:07.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:29:09.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:29:12.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:29:15.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:29:17.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:29:20.506 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:29:20.507 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:29:20.507 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 13:29:20.507 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:29:20.532 | 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

2025-09-01 13:29:20.533 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:29:20.629 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:29:20.773 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch176
2025-09-01 13:29:24.002 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 176/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.159s, 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: 1.608e-03, size: 576, ETA: 2:26:20
2025-09-01 13:29:27.384 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 176/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.167s, 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: 1.607e-03, size: 288, ETA: 2:26:17
2025-09-01 13:29:30.623 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 176/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.1, lr: 1.607e-03, size: 352, ETA: 2:26:13
2025-09-01 13:29:33.826 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 176/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.158s, 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: 1.606e-03, size: 352, ETA: 2:26:10
2025-09-01 13:29:37.051 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 176/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.606e-03, size: 352, ETA: 2:26:07
2025-09-01 13:29:40.292 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 176/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.160s, 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: 1.605e-03, size: 320, ETA: 2:26:04
2025-09-01 13:29:41.739 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:29:48.007 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:29:52.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:29:54.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5540
2025-09-01 13:29:55.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5035
2025-09-01 13:29:55.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3106
2025-09-01 13:29:55.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4560
2025-09-01 13:29:55.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:29:55.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:29:55.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.554
2025-09-01 13:29:55.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-09-01 13:29:55.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.311
2025-09-01 13:29:55.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.456
2025-09-01 13:29:55.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:29:55.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:29:55.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:29:55.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:29:55.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:29:55.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:29:55.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:29:55.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:29:55.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:29:58.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:30:02.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:30:05.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:30:08.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:30:12.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:30:15.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:30:19.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:30:22.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:30:25.746 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:30:25.746 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 13:30:25.746 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 13:30:25.746 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:30:25.772 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 1.01 ms, Average inference time: 7.30 ms

2025-09-01 13:30:25.773 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:30:25.855 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:30:25.941 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch177
2025-09-01 13:30:29.134 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 177/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.604e-03, size: 384, ETA: 2:25:59
2025-09-01 13:30:32.291 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 177/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.8, lr: 1.603e-03, size: 352, ETA: 2:25:55
2025-09-01 13:30:35.545 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 177/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.9, cls_loss: 1.0, lr: 1.603e-03, size: 480, ETA: 2:25:52
2025-09-01 13:30:38.816 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 177/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.161s, 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: 1.602e-03, size: 352, ETA: 2:25:49
2025-09-01 13:30:42.054 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 177/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.159s, 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: 1.601e-03, size: 384, ETA: 2:25:46
2025-09-01 13:30:45.322 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 177/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.601e-03, size: 480, ETA: 2:25:43
2025-09-01 13:30:46.742 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:30:53.119 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:30:56.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:30:58.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5530
2025-09-01 13:30:59.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4998
2025-09-01 13:30:59.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2839
2025-09-01 13:30:59.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4456
2025-09-01 13:30:59.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:30:59.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:30:59.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.553
2025-09-01 13:30:59.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-09-01 13:30:59.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.284
2025-09-01 13:30:59.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.446
2025-09-01 13:30:59.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:30:59.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:30:59.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:30:59.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:30:59.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:30:59.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:30:59.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:30:59.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:30:59.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:31:02.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:31:05.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:31:07.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:31:10.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:31:13.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:31:16.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:31:19.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:31:22.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:31:24.778 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:31:24.778 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:31:24.778 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:31:24.778 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:31:24.807 | 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

2025-09-01 13:31:24.808 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:31:24.893 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:31:25.045 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch178
2025-09-01 13:31:28.223 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 178/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.1, lr: 1.600e-03, size: 288, ETA: 2:25:38
2025-09-01 13:31:31.418 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 178/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.157s, 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: 1.599e-03, size: 288, ETA: 2:25:34
2025-09-01 13:31:34.584 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 178/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.598e-03, size: 288, ETA: 2:25:31
2025-09-01 13:31:37.729 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 178/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 0.9, lr: 1.598e-03, size: 288, ETA: 2:25:28
2025-09-01 13:31:40.926 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 178/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.158s, 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: 1.597e-03, size: 352, ETA: 2:25:24
2025-09-01 13:31:44.149 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 178/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.159s, 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: 1.597e-03, size: 320, ETA: 2:25:21
2025-09-01 13:31:45.692 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:31:51.942 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:31:54.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:31:55.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5509
2025-09-01 13:31:56.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5001
2025-09-01 13:31:56.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2955
2025-09-01 13:31:56.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4488
2025-09-01 13:31:56.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:31:56.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:31:56.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-09-01 13:31:56.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-09-01 13:31:56.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-09-01 13:31:56.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.449
2025-09-01 13:31:56.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:31:56.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:31:56.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:31:56.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:31:56.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:31:56.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:31:56.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:31:56.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:31:56.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:31:58.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:32:00.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:32:02.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:32:04.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:32:05.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:32:07.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:32:09.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:32:11.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:32:13.619 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:32:13.619 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:32:13.619 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:32:13.620 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:32:13.644 | 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

2025-09-01 13:32:13.646 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:32:13.727 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:32:13.894 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch179
2025-09-01 13:32:17.043 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 179/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.596e-03, size: 448, ETA: 2:25:16
2025-09-01 13:32:20.252 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 179/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.0, lr: 1.595e-03, size: 256, ETA: 2:25:13
2025-09-01 13:32:23.438 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 179/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 1.1, lr: 1.594e-03, size: 416, ETA: 2:25:10
2025-09-01 13:32:26.750 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 179/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 4.4, cls_loss: 1.0, lr: 1.594e-03, size: 576, ETA: 2:25:07
2025-09-01 13:32:30.104 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 179/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.165s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.2, lr: 1.593e-03, size: 320, ETA: 2:25:04
2025-09-01 13:32:33.279 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 179/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.9, lr: 1.592e-03, size: 512, ETA: 2:25:00
2025-09-01 13:32:34.729 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:32:40.861 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:32:43.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:32:45.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5341
2025-09-01 13:32:45.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4946
2025-09-01 13:32:45.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2967
2025-09-01 13:32:45.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4418
2025-09-01 13:32:45.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:32:45.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:32:45.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.534
2025-09-01 13:32:45.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-09-01 13:32:45.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.297
2025-09-01 13:32:45.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.442
2025-09-01 13:32:45.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:32:45.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:32:45.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:32:45.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:32:45.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:32:45.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:32:45.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:32:45.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:32:45.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:32:47.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:32:50.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:32:52.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:32:54.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:32:56.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:32:58.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:33:00.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:33:02.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:33:04.942 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:33:04.942 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:33:04.942 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:33:04.942 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:33:04.970 | 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

2025-09-01 13:33:04.971 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:33:05.065 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:33:05.154 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch180
2025-09-01 13:33:08.397 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 180/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.159s, 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: 1.591e-03, size: 288, ETA: 2:24:56
2025-09-01 13:33:11.694 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 180/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.162s, 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: 1.591e-03, size: 320, ETA: 2:24:52
2025-09-01 13:33:14.949 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 180/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.2Gb, 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: 1.590e-03, size: 480, ETA: 2:24:49
2025-09-01 13:33:18.273 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 180/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.2Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.589e-03, size: 544, ETA: 2:24:46
2025-09-01 13:33:21.667 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 180/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.167s, 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: 1.589e-03, size: 256, ETA: 2:24:43
2025-09-01 13:33:24.713 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 180/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.588e-03, size: 288, ETA: 2:24:40
2025-09-01 13:33:26.092 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:33:32.321 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:33:34.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:33:36.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5667
2025-09-01 13:33:36.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4979
2025-09-01 13:33:36.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2946
2025-09-01 13:33:36.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4530
2025-09-01 13:33:36.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:33:36.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:33:36.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.567
2025-09-01 13:33:36.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-09-01 13:33:36.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-09-01 13:33:36.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.453
2025-09-01 13:33:36.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:33:36.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:33:36.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:33:36.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:33:36.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:33:36.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:33:36.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:33:36.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:33:36.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:33:38.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:33:40.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:33:42.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:33:44.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:33:46.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:33:48.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:33:50.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:33:52.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:33:54.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:33:54.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 13:33:54.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:33:54.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:33:54.908 | 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

2025-09-01 13:33:54.911 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:33:55.093 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:33:55.179 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch181
2025-09-01 13:33:58.282 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 181/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 1.0, lr: 1.587e-03, size: 448, ETA: 2:24:35
2025-09-01 13:34:01.381 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 181/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.9, lr: 1.586e-03, size: 288, ETA: 2:24:31
2025-09-01 13:34:04.740 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 181/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.586e-03, size: 512, ETA: 2:24:28
2025-09-01 13:34:07.946 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 181/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.5, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.585e-03, size: 288, ETA: 2:24:25
2025-09-01 13:34:11.136 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 181/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.584e-03, size: 576, ETA: 2:24:21
2025-09-01 13:34:14.516 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 181/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.166s, 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: 1.584e-03, size: 256, ETA: 2:24:19
2025-09-01 13:34:16.002 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:34:22.267 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:34:23.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:34:24.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5361
2025-09-01 13:34:25.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4591
2025-09-01 13:34:25.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3096
2025-09-01 13:34:25.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4349
2025-09-01 13:34:25.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:34:25.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:34:25.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-09-01 13:34:25.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.459
2025-09-01 13:34:25.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.310
2025-09-01 13:34:25.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.435
2025-09-01 13:34:25.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:34:25.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:34:25.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:34:25.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:34:25.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:34:25.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:34:25.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:34:25.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:34:25.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:34:26.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:34:27.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:34:29.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:34:30.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:34:31.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:34:33.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:34:34.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:34:35.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:34:37.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:34:37.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-09-01 13:34:37.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 13:34:37.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:34:37.092 | 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

2025-09-01 13:34:37.093 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:34:37.171 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:34:37.256 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch182
2025-09-01 13:34:40.693 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 182/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.170s, 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: 1.583e-03, size: 320, ETA: 2:24:14
2025-09-01 13:34:43.964 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 182/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.0, lr: 1.582e-03, size: 576, ETA: 2:24:11
2025-09-01 13:34:47.273 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 182/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.163s, 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: 1.582e-03, size: 384, ETA: 2:24:08
2025-09-01 13:34:50.571 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 182/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.0, lr: 1.581e-03, size: 320, ETA: 2:24:05
2025-09-01 13:34:53.907 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 182/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.166s, 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: 1.580e-03, size: 416, ETA: 2:24:02
2025-09-01 13:34:57.160 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 182/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.580e-03, size: 288, ETA: 2:23:59
2025-09-01 13:34:58.648 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:35:05.110 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:35:07.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:35:09.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5644
2025-09-01 13:35:09.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5043
2025-09-01 13:35:10.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3138
2025-09-01 13:35:10.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4608
2025-09-01 13:35:10.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:35:10.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:35:10.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.564
2025-09-01 13:35:10.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-09-01 13:35:10.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-09-01 13:35:10.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.461
2025-09-01 13:35:10.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:35:10.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:35:10.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:35:10.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:35:10.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:35:10.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:35:10.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:35:10.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:35:10.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:35:12.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:35:14.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:35:16.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:35:19.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:35:21.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:35:23.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:35:26.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:35:28.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:35:30.467 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:35:30.467 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 13:35:30.467 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 13:35:30.467 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:35:30.495 | 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: 7.12 ms

2025-09-01 13:35:30.496 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:35:30.642 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:35:30.756 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch183
2025-09-01 13:35:33.837 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 183/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.579e-03, size: 256, ETA: 2:23:54
2025-09-01 13:35:36.974 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 183/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.578e-03, size: 288, ETA: 2:23:51
2025-09-01 13:35:40.113 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 183/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.577e-03, size: 384, ETA: 2:23:47
2025-09-01 13:35:43.328 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 183/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, 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: 1.577e-03, size: 448, ETA: 2:23:44
2025-09-01 13:35:46.674 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 183/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.576e-03, size: 480, ETA: 2:23:41
2025-09-01 13:35:49.821 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 183/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 8.9, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 4.6, cls_loss: 0.9, lr: 1.575e-03, size: 320, ETA: 2:23:37
2025-09-01 13:35:51.231 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:35:57.629 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:36:00.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:36:02.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5322
2025-09-01 13:36:03.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4611
2025-09-01 13:36:03.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2430
2025-09-01 13:36:03.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4121
2025-09-01 13:36:03.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:36:03.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:36:03.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-09-01 13:36:03.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.461
2025-09-01 13:36:03.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.243
2025-09-01 13:36:03.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.412
2025-09-01 13:36:03.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:36:03.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:36:03.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:36:03.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:36:03.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:36:03.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:36:03.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:36:03.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:36:03.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:36:06.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:36:08.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:36:11.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:36:13.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:36:16.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:36:19.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:36:21.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:36:24.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:36:26.708 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:36:26.708 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:36:26.708 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-09-01 13:36:26.708 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:36:26.734 | 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

2025-09-01 13:36:26.735 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:36:26.867 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:36:26.955 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch184
2025-09-01 13:36:30.137 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 184/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.158s, 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: 1.574e-03, size: 512, ETA: 2:23:33
2025-09-01 13:36:33.422 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 184/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.574e-03, size: 416, ETA: 2:23:30
2025-09-01 13:36:36.727 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 184/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.7, lr: 1.573e-03, size: 512, ETA: 2:23:26
2025-09-01 13:36:39.994 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 184/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.572e-03, size: 416, ETA: 2:23:23
2025-09-01 13:36:43.139 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 184/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, 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: 1.572e-03, size: 512, ETA: 2:23:20
2025-09-01 13:36:46.530 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 184/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.168s, 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: 1.571e-03, size: 384, ETA: 2:23:17
2025-09-01 13:36:47.969 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:36:54.187 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:36:57.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:36:59.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5743
2025-09-01 13:36:59.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4662
2025-09-01 13:37:00.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2770
2025-09-01 13:37:00.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4392
2025-09-01 13:37:00.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:37:00.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:37:00.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-09-01 13:37:00.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-09-01 13:37:00.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.277
2025-09-01 13:37:00.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.439
2025-09-01 13:37:00.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:37:00.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:37:00.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:37:00.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:37:00.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:37:00.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:37:00.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:37:00.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:37:00.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:37:02.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:37:05.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:37:07.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:37:10.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:37:13.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:37:15.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:37:18.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:37:21.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:37:23.727 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:37:23.727 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:37:23.727 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:37:23.727 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:37:23.753 | 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

2025-09-01 13:37:23.754 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:37:23.830 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:37:23.923 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch185
2025-09-01 13:37:27.082 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 185/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, 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: 1.570e-03, size: 384, ETA: 2:23:12
2025-09-01 13:37:30.202 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 185/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.3, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 1.1, lr: 1.569e-03, size: 288, ETA: 2:23:09
2025-09-01 13:37:33.351 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 185/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.569e-03, size: 448, ETA: 2:23:05
2025-09-01 13:37:36.595 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 185/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.568e-03, size: 352, ETA: 2:23:02
2025-09-01 13:37:39.849 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 185/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.567e-03, size: 480, ETA: 2:22:59
2025-09-01 13:37:43.096 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 185/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, 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: 1.567e-03, size: 448, ETA: 2:22:56
2025-09-01 13:37:44.571 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:37:50.742 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:37:53.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:37:54.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5643
2025-09-01 13:37:55.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4946
2025-09-01 13:37:55.139 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2741
2025-09-01 13:37:55.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4443
2025-09-01 13:37:55.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:37:55.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:37:55.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.564
2025-09-01 13:37:55.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-09-01 13:37:55.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.274
2025-09-01 13:37:55.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.444
2025-09-01 13:37:55.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:37:55.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:37:55.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:37:55.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:37:55.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:37:55.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:37:55.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:37:55.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:37:55.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:37:57.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:37:59.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:38:01.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:38:03.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:38:05.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:38:07.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:38:09.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:38:11.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:38:13.113 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:38:13.114 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:38:13.114 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:38:13.114 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:38:13.139 | 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

2025-09-01 13:38:13.141 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:38:13.216 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:38:13.303 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch186
2025-09-01 13:38:16.447 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 186/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, 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: 1.566e-03, size: 512, ETA: 2:22:51
2025-09-01 13:38:19.715 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 186/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.565e-03, size: 384, ETA: 2:22:48
2025-09-01 13:38:22.893 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 186/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.7, lr: 1.564e-03, size: 288, ETA: 2:22:44
2025-09-01 13:38:26.252 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 186/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.165s, 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: 1.564e-03, size: 544, ETA: 2:22:41
2025-09-01 13:38:29.679 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 186/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.170s, 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: 1.563e-03, size: 416, ETA: 2:22:39
2025-09-01 13:38:32.886 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 186/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.9, lr: 1.562e-03, size: 544, ETA: 2:22:35
2025-09-01 13:38:34.316 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:38:40.526 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:38:42.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:38:44.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5646
2025-09-01 13:38:44.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5125
2025-09-01 13:38:44.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2651
2025-09-01 13:38:44.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4474
2025-09-01 13:38:44.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:38:44.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:38:44.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.565
2025-09-01 13:38:44.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-09-01 13:38:44.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.265
2025-09-01 13:38:44.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.447
2025-09-01 13:38:44.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:38:44.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:38:44.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:38:44.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:38:44.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:38:44.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:38:44.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:38:44.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:38:44.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:38:46.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:38:48.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:38:50.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:38:51.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:38:53.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:38:55.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:38:57.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:38:59.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:39:01.143 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:39:01.143 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:39:01.143 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:39:01.143 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:39:01.168 | 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

2025-09-01 13:39:01.170 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:39:01.251 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:39:01.340 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch187
2025-09-01 13:39:04.412 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 187/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, 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: 1.561e-03, size: 352, ETA: 2:22:30
2025-09-01 13:39:07.556 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 187/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.561e-03, size: 256, ETA: 2:22:27
2025-09-01 13:39:10.784 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 187/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.2, lr: 1.560e-03, size: 384, ETA: 2:22:24
2025-09-01 13:39:14.113 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 187/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.559e-03, size: 288, ETA: 2:22:21
2025-09-01 13:39:17.340 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 187/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.159s, 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: 320, ETA: 2:22:17
2025-09-01 13:39:20.631 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 187/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.162s, 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: 1.558e-03, size: 480, ETA: 2:22:14
2025-09-01 13:39:22.058 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:39:28.423 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:39:32.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:39:35.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5609
2025-09-01 13:39:35.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4965
2025-09-01 13:39:36.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2965
2025-09-01 13:39:36.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4513
2025-09-01 13:39:36.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:39:36.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:39:36.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.561
2025-09-01 13:39:36.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-09-01 13:39:36.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.297
2025-09-01 13:39:36.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.451
2025-09-01 13:39:36.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:39:36.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:39:36.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:39:36.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:39:36.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:39:36.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:39:36.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:39:36.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:39:36.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:39:39.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:39:42.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:39:46.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:39:49.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:39:53.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:39:56.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:40:00.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:40:03.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:40:07.327 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:40:07.328 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:40:07.328 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:40:07.328 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:40:07.359 | 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: 7.19 ms

2025-09-01 13:40:07.360 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:40:07.475 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:40:07.602 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch188
2025-09-01 13:40:10.827 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 188/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.159s, data_time: 0.005s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 1.1, lr: 1.557e-03, size: 576, ETA: 2:22:09
2025-09-01 13:40:14.113 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 188/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.556e-03, size: 416, ETA: 2:22:06
2025-09-01 13:40:17.286 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 188/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.556e-03, size: 512, ETA: 2:22:03
2025-09-01 13:40:20.579 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 188/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, 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: 0.9, lr: 1.555e-03, size: 480, ETA: 2:22:00
2025-09-01 13:40:23.757 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 188/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.9, lr: 1.554e-03, size: 320, ETA: 2:21:57
2025-09-01 13:40:27.004 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 188/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.6, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 0.6, lr: 1.554e-03, size: 448, ETA: 2:21:53
2025-09-01 13:40:28.528 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:40:34.768 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:40:38.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:40:41.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5653
2025-09-01 13:40:41.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4921
2025-09-01 13:40:41.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3021
2025-09-01 13:40:41.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4532
2025-09-01 13:40:41.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:40:41.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:40:41.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.565
2025-09-01 13:40:41.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.492
2025-09-01 13:40:41.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.302
2025-09-01 13:40:41.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.453
2025-09-01 13:40:41.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:40:41.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:40:41.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:40:41.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:40:41.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:40:41.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:40:41.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:40:41.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:40:41.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:40:45.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:40:48.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:40:51.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:40:55.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:40:58.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:41:01.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:41:04.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:41:08.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:41:11.454 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:41:11.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:41:11.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:41:11.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:41:11.480 | 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

2025-09-01 13:41:11.483 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:41:11.563 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:41:11.650 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch189
2025-09-01 13:41:14.885 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 189/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.161s, 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: 1.553e-03, size: 256, ETA: 2:21:49
2025-09-01 13:41:18.178 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 189/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.163s, 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: 1.552e-03, size: 352, ETA: 2:21:46
2025-09-01 13:41:21.421 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 189/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.160s, 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: 1.551e-03, size: 544, ETA: 2:21:42
2025-09-01 13:41:24.664 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 189/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.551e-03, size: 480, ETA: 2:21:39
2025-09-01 13:41:27.855 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 189/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 1.0, lr: 1.550e-03, size: 576, ETA: 2:21:36
2025-09-01 13:41:31.167 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 189/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.549e-03, size: 512, ETA: 2:21:33
2025-09-01 13:41:32.670 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:41:38.803 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:41:42.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:41:45.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5307
2025-09-01 13:41:45.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4446
2025-09-01 13:41:45.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3204
2025-09-01 13:41:45.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4319
2025-09-01 13:41:45.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:41:45.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:41:45.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-09-01 13:41:45.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.445
2025-09-01 13:41:45.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.320
2025-09-01 13:41:45.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.432
2025-09-01 13:41:45.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:41:45.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:41:45.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:41:45.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:41:45.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:41:45.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:41:45.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:41:45.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:41:45.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:41:49.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:41:52.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:41:55.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:41:58.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:42:02.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:42:05.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:42:08.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:42:12.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:42:15.419 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:42:15.420 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-09-01 13:42:15.420 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 13:42:15.420 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:42:15.453 | 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

2025-09-01 13:42:15.458 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:42:15.611 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:42:15.697 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch190
2025-09-01 13:42:18.760 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 190/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.0, cls_loss: 1.0, lr: 1.548e-03, size: 384, ETA: 2:21:28
2025-09-01 13:42:21.924 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 190/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.157s, 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: 1.548e-03, size: 384, ETA: 2:21:25
2025-09-01 13:42:25.130 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 190/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.157s, 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: 1.547e-03, size: 288, ETA: 2:21:21
2025-09-01 13:42:28.255 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 190/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, 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: 1.546e-03, size: 416, ETA: 2:21:18
2025-09-01 13:42:31.478 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 190/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.546e-03, size: 416, ETA: 2:21:15
2025-09-01 13:42:34.755 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 190/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.161s, data_time: 0.001s, total_loss: 6.5, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.545e-03, size: 288, ETA: 2:21:11
2025-09-01 13:42:36.188 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:42:42.407 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:42:44.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:42:46.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5565
2025-09-01 13:42:46.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4841
2025-09-01 13:42:46.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3208
2025-09-01 13:42:46.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4538
2025-09-01 13:42:46.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:42:46.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:42:46.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.557
2025-09-01 13:42:46.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-09-01 13:42:46.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.321
2025-09-01 13:42:46.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.454
2025-09-01 13:42:46.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:42:46.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:42:46.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:42:46.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:42:46.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:42:46.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:42:46.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:42:46.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:42:46.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:42:48.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:42:50.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:42:52.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:42:54.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:42:56.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:42:58.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:43:00.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:43:02.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:43:04.760 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:43:04.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:43:04.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:43:04.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:43:04.787 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.95 ms, Average inference time: 7.26 ms

2025-09-01 13:43:04.788 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:43:04.871 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:43:04.966 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch191
2025-09-01 13:43:08.019 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 191/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.544e-03, size: 416, ETA: 2:21:06
2025-09-01 13:43:11.295 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 191/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.543e-03, size: 512, ETA: 2:21:03
2025-09-01 13:43:14.709 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 191/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.168s, 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: 1.543e-03, size: 480, ETA: 2:21:00
2025-09-01 13:43:17.917 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 191/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.158s, 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: 1.542e-03, size: 352, ETA: 2:20:57
2025-09-01 13:43:21.210 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 191/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 1.1, lr: 1.541e-03, size: 544, ETA: 2:20:54
2025-09-01 13:43:24.417 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 191/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, 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: 1.541e-03, size: 352, ETA: 2:20:51
2025-09-01 13:43:25.876 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:43:32.209 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:43:35.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:43:37.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5507
2025-09-01 13:43:37.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4881
2025-09-01 13:43:37.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3187
2025-09-01 13:43:37.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4525
2025-09-01 13:43:37.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:43:37.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:43:37.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.551
2025-09-01 13:43:37.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.488
2025-09-01 13:43:37.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.319
2025-09-01 13:43:37.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.452
2025-09-01 13:43:37.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:43:37.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:43:37.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:43:37.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:43:37.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:43:37.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:43:37.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:43:37.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:43:37.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:43:40.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:43:42.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:43:45.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:43:48.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:43:50.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:43:53.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:43:55.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:43:58.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:44:00.929 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:44:00.929 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:44:00.930 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:44:00.930 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:44:00.957 | 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: 7.14 ms

2025-09-01 13:44:00.958 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:44:01.048 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:44:01.196 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch192
2025-09-01 13:44:04.311 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 192/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 8.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 4.2, cls_loss: 1.2, lr: 1.540e-03, size: 448, ETA: 2:20:46
2025-09-01 13:44:07.523 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 192/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.539e-03, size: 256, ETA: 2:20:42
2025-09-01 13:44:10.721 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 192/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.158s, 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: 1.538e-03, size: 576, ETA: 2:20:39
2025-09-01 13:44:13.948 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 192/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.9, lr: 1.538e-03, size: 352, ETA: 2:20:36
2025-09-01 13:44:17.180 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 192/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.537e-03, size: 448, ETA: 2:20:33
2025-09-01 13:44:20.537 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 192/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.166s, 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: 1.536e-03, size: 384, ETA: 2:20:30
2025-09-01 13:44:22.018 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:44:28.394 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:44:30.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:44:32.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5666
2025-09-01 13:44:32.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4904
2025-09-01 13:44:32.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3191
2025-09-01 13:44:32.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4587
2025-09-01 13:44:32.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:44:32.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:44:32.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.567
2025-09-01 13:44:32.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-09-01 13:44:32.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.319
2025-09-01 13:44:32.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.459
2025-09-01 13:44:32.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:44:32.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:44:32.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:44:32.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:44:32.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:44:32.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:44:32.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:44:32.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:44:32.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:44:34.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:44:36.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:44:38.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:44:40.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:44:42.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:44:44.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:44:45.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:44:47.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:44:49.730 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:44:49.730 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:44:49.730 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 13:44:49.731 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:44:49.759 | 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

2025-09-01 13:44:49.760 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:44:49.886 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:44:49.978 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch193
2025-09-01 13:44:53.159 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 193/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, 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: 1.535e-03, size: 416, ETA: 2:20:25
2025-09-01 13:44:56.396 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 193/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.6, lr: 1.534e-03, size: 576, ETA: 2:20:22
2025-09-01 13:44:59.795 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 193/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 1.0, lr: 1.534e-03, size: 480, ETA: 2:20:19
2025-09-01 13:45:03.015 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 193/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.533e-03, size: 288, ETA: 2:20:16
2025-09-01 13:45:06.459 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 193/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.169s, 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: 1.532e-03, size: 384, ETA: 2:20:13
2025-09-01 13:45:09.655 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 193/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.157s, 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: 1.532e-03, size: 256, ETA: 2:20:09
2025-09-01 13:45:11.110 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:45:17.289 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:45:20.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:45:23.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5448
2025-09-01 13:45:23.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4841
2025-09-01 13:45:23.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2749
2025-09-01 13:45:23.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4346
2025-09-01 13:45:23.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:45:23.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:45:23.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-09-01 13:45:23.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-09-01 13:45:23.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.275
2025-09-01 13:45:23.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.435
2025-09-01 13:45:23.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:45:23.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:45:23.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:45:23.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:45:23.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:45:23.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:45:23.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:45:23.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:45:23.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:45:26.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:45:29.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:45:32.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:45:35.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:45:37.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:45:40.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:45:43.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:45:46.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:45:49.285 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:45:49.285 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 13:45:49.285 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 13:45:49.285 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:45:49.318 | 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

2025-09-01 13:45:49.320 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:45:49.445 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:45:49.561 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch194
2025-09-01 13:45:52.748 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 194/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.5, cls_loss: 0.9, lr: 1.531e-03, size: 352, ETA: 2:20:05
2025-09-01 13:45:55.846 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 194/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 2.1, cls_loss: 0.6, lr: 1.530e-03, size: 288, ETA: 2:20:01
2025-09-01 13:45:59.203 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 194/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.165s, 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: 1.529e-03, size: 256, ETA: 2:19:58
2025-09-01 13:46:02.432 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 194/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 2.8, cls_loss: 0.9, lr: 1.529e-03, size: 320, ETA: 2:19:55
2025-09-01 13:46:05.728 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 194/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.528e-03, size: 576, ETA: 2:19:52
2025-09-01 13:46:08.940 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 194/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.8, lr: 1.527e-03, size: 288, ETA: 2:19:49
2025-09-01 13:46:10.408 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:46:16.733 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:46:18.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:46:19.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5684
2025-09-01 13:46:20.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4821
2025-09-01 13:46:20.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2985
2025-09-01 13:46:20.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4497
2025-09-01 13:46:20.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:46:20.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:46:20.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.568
2025-09-01 13:46:20.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-09-01 13:46:20.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.299
2025-09-01 13:46:20.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.450
2025-09-01 13:46:20.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:46:20.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:46:20.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:46:20.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:46:20.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:46:20.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:46:20.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:46:20.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:46:20.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:46:21.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:46:23.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:46:24.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:46:26.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:46:27.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:46:29.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:46:30.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:46:32.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:46:34.043 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:46:34.043 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:46:34.043 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:46:34.043 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:46:34.068 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.37 ms, Average NMS time: 0.96 ms, Average inference time: 7.33 ms

2025-09-01 13:46:34.069 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:46:34.152 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:46:34.239 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch195
2025-09-01 13:46:37.407 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 195/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.526e-03, size: 544, ETA: 2:19:44
2025-09-01 13:46:40.728 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 195/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 1.2, lr: 1.526e-03, size: 416, ETA: 2:19:41
2025-09-01 13:46:44.002 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 195/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 4.1, cls_loss: 0.9, lr: 1.525e-03, size: 576, ETA: 2:19:38
2025-09-01 13:46:47.392 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 195/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.524e-03, size: 288, ETA: 2:19:35
2025-09-01 13:46:50.617 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 195/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 1.1, lr: 1.524e-03, size: 256, ETA: 2:19:31
2025-09-01 13:46:53.892 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 195/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.523e-03, size: 576, ETA: 2:19:28
2025-09-01 13:46:55.417 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:47:01.646 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:47:05.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:47:08.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5360
2025-09-01 13:47:08.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4764
2025-09-01 13:47:08.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2711
2025-09-01 13:47:08.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4278
2025-09-01 13:47:08.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:47:08.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:47:08.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-09-01 13:47:08.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-09-01 13:47:08.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.271
2025-09-01 13:47:08.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.428
2025-09-01 13:47:08.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:47:08.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:47:08.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:47:08.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:47:08.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:47:08.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:47:08.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:47:08.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:47:08.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:47:11.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:47:14.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:47:17.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:47:20.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:47:24.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:47:27.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:47:30.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:47:33.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:47:36.319 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:47:36.319 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-09-01 13:47:36.319 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 13:47:36.319 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:47:36.345 | 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

2025-09-01 13:47:36.347 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:47:36.427 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:47:36.514 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch196
2025-09-01 13:47:39.757 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 196/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.159s, data_time: 0.005s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.522e-03, size: 448, ETA: 2:19:24
2025-09-01 13:47:43.016 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 196/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.521e-03, size: 544, ETA: 2:19:20
2025-09-01 13:47:46.253 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 196/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.160s, 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: 1.520e-03, size: 320, ETA: 2:19:17
2025-09-01 13:47:49.673 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 196/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.168s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.520e-03, size: 352, ETA: 2:19:14
2025-09-01 13:47:52.872 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 196/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.519e-03, size: 448, ETA: 2:19:11
2025-09-01 13:47:56.159 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 196/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, 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: 0.8, lr: 1.518e-03, size: 512, ETA: 2:19:08
2025-09-01 13:47:57.638 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:48:04.055 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:48:06.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:48:08.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5597
2025-09-01 13:48:08.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4887
2025-09-01 13:48:09.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3050
2025-09-01 13:48:09.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4512
2025-09-01 13:48:09.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:48:09.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:48:09.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.560
2025-09-01 13:48:09.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-09-01 13:48:09.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.305
2025-09-01 13:48:09.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.451
2025-09-01 13:48:09.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:48:09.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:48:09.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:48:09.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:48:09.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:48:09.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:48:09.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:48:09.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:48:09.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:48:11.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:48:13.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:48:16.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:48:18.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:48:20.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:48:22.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:48:25.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:48:27.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:48:29.780 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:48:29.780 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:48:29.780 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:48:29.780 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:48:29.806 | 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

2025-09-01 13:48:29.807 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:48:29.922 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:48:30.069 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch197
2025-09-01 13:48:33.233 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 197/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.0, conf_loss: 3.4, cls_loss: 0.8, lr: 1.517e-03, size: 448, ETA: 2:19:03
2025-09-01 13:48:36.442 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 197/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, 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: 1.517e-03, size: 384, ETA: 2:19:00
2025-09-01 13:48:39.746 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 197/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.162s, 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: 1.516e-03, size: 544, ETA: 2:18:57
2025-09-01 13:48:42.965 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 197/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.159s, 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: 1.515e-03, size: 320, ETA: 2:18:54
2025-09-01 13:48:46.231 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 197/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.3, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.515e-03, size: 256, ETA: 2:18:50
2025-09-01 13:48:49.479 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 197/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.160s, 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: 1.514e-03, size: 544, ETA: 2:18:47
2025-09-01 13:48:50.978 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:48:57.120 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:48:59.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:49:00.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5534
2025-09-01 13:49:00.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4776
2025-09-01 13:49:00.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2565
2025-09-01 13:49:00.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4292
2025-09-01 13:49:00.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:49:00.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:49:00.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.553
2025-09-01 13:49:00.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.478
2025-09-01 13:49:00.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.256
2025-09-01 13:49:00.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.429
2025-09-01 13:49:00.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:49:00.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:49:00.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:49:00.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:49:00.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:49:00.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:49:00.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:49:00.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:49:00.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:49:02.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:49:03.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:49:05.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:49:06.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:49:08.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:49:09.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:49:11.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:49:12.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:49:14.113 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:49:14.114 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 13:49:14.114 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 13:49:14.114 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:49:14.142 | 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

2025-09-01 13:49:14.144 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:49:14.261 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:49:14.362 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch198
2025-09-01 13:49:17.660 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 198/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.8, l1_loss: 0.0, conf_loss: 2.3, cls_loss: 0.8, lr: 1.513e-03, size: 448, ETA: 2:18:43
2025-09-01 13:49:20.946 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 198/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.163s, 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: 1.512e-03, size: 320, ETA: 2:18:40
2025-09-01 13:49:24.252 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 198/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.1, cls_loss: 0.9, lr: 1.511e-03, size: 512, ETA: 2:18:37
2025-09-01 13:49:27.618 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 198/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.166s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.1, l1_loss: 0.0, conf_loss: 3.8, cls_loss: 0.8, lr: 1.511e-03, size: 448, ETA: 2:18:34
2025-09-01 13:49:30.959 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 198/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.2, l1_loss: 0.0, conf_loss: 2.9, cls_loss: 0.9, lr: 1.510e-03, size: 288, ETA: 2:18:31
2025-09-01 13:49:34.199 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 198/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.160s, 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: 1.509e-03, size: 480, ETA: 2:18:27
2025-09-01 13:49:35.636 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:49:42.014 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:49:44.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:49:45.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5785
2025-09-01 13:49:45.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5068
2025-09-01 13:49:46.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3136
2025-09-01 13:49:46.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4663
2025-09-01 13:49:46.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:49:46.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:49:46.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-09-01 13:49:46.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-09-01 13:49:46.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.314
2025-09-01 13:49:46.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.466
2025-09-01 13:49:46.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:49:46.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:49:46.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:49:46.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:49:46.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:49:46.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:49:46.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:49:46.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:49:46.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:49:47.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:49:49.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:49:51.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:49:53.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:49:55.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:49:57.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:49:59.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:50:01.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:50:03.014 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:50:03.014 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 13:50:03.014 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 13:50:03.014 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:50:03.043 | 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: 7.14 ms

2025-09-01 13:50:03.044 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:50:03.163 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:50:03.296 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch199
2025-09-01 13:50:06.533 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 199/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.161s, data_time: 0.005s, total_loss: 6.3, iou_loss: 2.9, l1_loss: 0.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.508e-03, size: 448, ETA: 2:18:23
2025-09-01 13:50:09.965 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 199/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.169s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.2, cls_loss: 1.0, lr: 1.508e-03, size: 352, ETA: 2:18:20
2025-09-01 13:50:13.173 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 199/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 0.0, conf_loss: 3.3, cls_loss: 1.0, lr: 1.507e-03, size: 512, ETA: 2:18:17
2025-09-01 13:50:16.424 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 199/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.161s, 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: 1.506e-03, size: 384, ETA: 2:18:13
2025-09-01 13:50:19.705 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 199/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, 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: 1.0, lr: 1.506e-03, size: 448, ETA: 2:18:10
2025-09-01 13:50:23.101 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 199/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, 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: 0.8, lr: 1.505e-03, size: 288, ETA: 2:18:07
2025-09-01 13:50:24.539 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:50:30.721 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:50:34.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:50:37.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5340
2025-09-01 13:50:37.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4961
2025-09-01 13:50:37.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2814
2025-09-01 13:50:37.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4371
2025-09-01 13:50:37.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:50:37.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:50:37.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.534
2025-09-01 13:50:37.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-09-01 13:50:37.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.281
2025-09-01 13:50:37.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.437
2025-09-01 13:50:37.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:50:37.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:50:37.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:50:37.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:50:37.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:50:37.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:50:37.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:50:37.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:50:37.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:50:40.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:50:43.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:50:46.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:50:50.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:50:53.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:50:56.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:50:59.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:51:02.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:51:05.295 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:51:05.295 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 13:51:05.295 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:51:05.295 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:51:05.321 | 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: 7.18 ms

2025-09-01 13:51:05.324 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:51:05.419 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:51:05.543 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch200
2025-09-01 13:51:05.544 | INFO     | yolox_microbt.core.trainer:before_epoch:208 - --->No mosaic aug now!
2025-09-01 13:51:05.544 | INFO     | yolox_microbt.core.trainer:before_epoch:210 - --->Add additional L1 loss now!
2025-09-01 13:51:05.544 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:51:08.695 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 200/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 11.3, iou_loss: 3.1, l1_loss: 1.6, conf_loss: 5.4, cls_loss: 1.2, lr: 1.504e-03, size: 544, ETA: 2:18:02
2025-09-01 13:51:11.671 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 200/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 1.503e-03, size: 320, ETA: 2:17:58
2025-09-01 13:51:14.811 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 200/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 1.502e-03, size: 320, ETA: 2:17:55
2025-09-01 13:51:17.861 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 200/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.5, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 4.1, cls_loss: 0.6, lr: 1.502e-03, size: 576, ETA: 2:17:51
2025-09-01 13:51:20.999 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 200/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.2, cls_loss: 0.8, lr: 1.501e-03, size: 512, ETA: 2:17:48
2025-09-01 13:51:24.056 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 200/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.500e-03, size: 320, ETA: 2:17:44
2025-09-01 13:51:25.334 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:51:31.528 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:51:32.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:51:32.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5552
2025-09-01 13:51:33.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5035
2025-09-01 13:51:33.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2936
2025-09-01 13:51:33.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4508
2025-09-01 13:51:33.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:51:33.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:51:33.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.555
2025-09-01 13:51:33.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-09-01 13:51:33.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.294
2025-09-01 13:51:33.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.451
2025-09-01 13:51:33.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:51:33.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:51:33.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:51:33.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:51:33.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:51:33.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:51:33.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:51:33.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:51:33.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:51:33.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:51:34.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:51:35.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:51:36.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:51:36.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:51:37.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:51:38.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:51:39.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:51:39.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:51:39.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 13:51:39.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:51:39.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:51:39.903 | 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

2025-09-01 13:51:39.905 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:51:39.994 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:51:40.077 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch201
2025-09-01 13:51:43.113 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 201/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.499e-03, size: 256, ETA: 2:17:39
2025-09-01 13:51:46.104 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 201/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 1.499e-03, size: 288, ETA: 2:17:35
2025-09-01 13:51:49.122 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 201/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.7, lr: 1.498e-03, size: 480, ETA: 2:17:32
2025-09-01 13:51:52.090 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 201/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 1.497e-03, size: 352, ETA: 2:17:28
2025-09-01 13:51:55.056 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 201/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.8, lr: 1.496e-03, size: 288, ETA: 2:17:24
2025-09-01 13:51:57.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 201/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.8, cls_loss: 0.6, lr: 1.496e-03, size: 320, ETA: 2:17:20
2025-09-01 13:51:59.326 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:52:05.731 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:52:06.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:52:07.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5432
2025-09-01 13:52:07.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4922
2025-09-01 13:52:07.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3268
2025-09-01 13:52:07.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4541
2025-09-01 13:52:07.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:52:07.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:52:07.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-09-01 13:52:07.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.492
2025-09-01 13:52:07.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.327
2025-09-01 13:52:07.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.454
2025-09-01 13:52:07.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:52:07.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:52:07.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:52:07.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:52:07.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:52:07.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:52:07.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:52:07.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:52:07.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:52:08.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:52:09.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:52:10.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:52:11.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:52:12.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:52:13.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:52:14.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:52:15.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:52:15.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:52:15.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 13:52:15.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:52:15.953 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:52:15.961 | 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

2025-09-01 13:52:15.962 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:52:16.049 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:52:16.133 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch202
2025-09-01 13:52:18.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 202/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.6, cls_loss: 1.0, lr: 1.495e-03, size: 288, ETA: 2:17:15
2025-09-01 13:52:21.902 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 202/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.8, lr: 1.494e-03, size: 256, ETA: 2:17:11
2025-09-01 13:52:24.965 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 202/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.493e-03, size: 448, ETA: 2:17:07
2025-09-01 13:52:28.140 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 202/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.9, lr: 1.493e-03, size: 256, ETA: 2:17:04
2025-09-01 13:52:31.081 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 202/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.492e-03, size: 256, ETA: 2:17:00
2025-09-01 13:52:34.107 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 202/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, 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: 1.491e-03, size: 256, ETA: 2:16:57
2025-09-01 13:52:35.450 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:52:41.824 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:52:42.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:52:43.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5484
2025-09-01 13:52:43.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4965
2025-09-01 13:52:43.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3118
2025-09-01 13:52:43.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4522
2025-09-01 13:52:43.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:52:43.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:52:43.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.548
2025-09-01 13:52:43.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-09-01 13:52:43.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.312
2025-09-01 13:52:43.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.452
2025-09-01 13:52:43.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:52:43.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:52:43.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:52:43.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:52:43.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:52:43.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:52:43.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:52:43.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:52:43.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:52:44.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:52:44.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:52:45.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:52:46.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:52:47.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:52:47.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:52:48.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:52:49.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:52:49.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:52:49.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 13:52:49.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:52:49.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:52:49.959 | 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

2025-09-01 13:52:49.960 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:52:50.049 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:52:50.132 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch203
2025-09-01 13:52:52.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 203/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.141s, data_time: 0.004s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 2.0, cls_loss: 0.6, lr: 1.490e-03, size: 256, ETA: 2:16:51
2025-09-01 13:52:56.033 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 203/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.9, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 4.0, cls_loss: 0.7, lr: 1.489e-03, size: 576, ETA: 2:16:47
2025-09-01 13:52:59.041 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 203/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 1.489e-03, size: 384, ETA: 2:16:44
2025-09-01 13:53:02.037 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 203/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.5, lr: 1.488e-03, size: 512, ETA: 2:16:40
2025-09-01 13:53:04.974 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 203/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 1.487e-03, size: 352, ETA: 2:16:36
2025-09-01 13:53:08.004 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 203/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 8.5, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.7, cls_loss: 0.7, lr: 1.487e-03, size: 448, ETA: 2:16:33
2025-09-01 13:53:09.371 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:53:15.513 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:53:16.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:53:16.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5373
2025-09-01 13:53:16.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4693
2025-09-01 13:53:16.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2479
2025-09-01 13:53:16.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4181
2025-09-01 13:53:16.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:53:16.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:53:16.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-09-01 13:53:16.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-09-01 13:53:16.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.248
2025-09-01 13:53:16.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.418
2025-09-01 13:53:16.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:53:16.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:53:16.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:53:16.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:53:16.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:53:16.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:53:16.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:53:16.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:53:16.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:53:17.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:53:17.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:53:18.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:53:18.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:53:19.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:53:19.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:53:20.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:53:20.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:53:21.102 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:53:21.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:53:21.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-09-01 13:53:21.103 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:53:21.110 | 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

2025-09-01 13:53:21.112 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:53:21.240 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:53:21.325 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch204
2025-09-01 13:53:24.208 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 204/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 2.1, cls_loss: 0.8, lr: 1.486e-03, size: 512, ETA: 2:16:27
2025-09-01 13:53:27.181 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 204/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.2, iou_loss: 1.5, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 1.4, lr: 1.485e-03, size: 384, ETA: 2:16:23
2025-09-01 13:53:30.142 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 204/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 1.484e-03, size: 544, ETA: 2:16:20
2025-09-01 13:53:33.233 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 204/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.5, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 1.483e-03, size: 352, ETA: 2:16:16
2025-09-01 13:53:36.207 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 204/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 1.483e-03, size: 576, ETA: 2:16:12
2025-09-01 13:53:39.289 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 204/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 1.482e-03, size: 480, ETA: 2:16:09
2025-09-01 13:53:40.609 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:53:46.873 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:53:47.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:53:48.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5724
2025-09-01 13:53:48.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5320
2025-09-01 13:53:48.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3715
2025-09-01 13:53:48.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4919
2025-09-01 13:53:48.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:53:48.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:53:48.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-09-01 13:53:48.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-09-01 13:53:48.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-09-01 13:53:48.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-09-01 13:53:48.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:53:48.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:53:48.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:53:48.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:53:48.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:53:48.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:53:48.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:53:48.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:53:48.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:53:48.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:53:49.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:53:50.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:53:51.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:53:51.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:53:52.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:53:53.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:53:53.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:53:54.321 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:53:54.322 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 13:53:54.322 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 13:53:54.322 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:53:54.329 | 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

2025-09-01 13:53:54.330 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:53:54.418 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:53:54.538 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch205
2025-09-01 13:53:57.467 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 205/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.144s, 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: 1.481e-03, size: 352, ETA: 2:16:03
2025-09-01 13:54:00.495 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 205/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 1.480e-03, size: 384, ETA: 2:16:00
2025-09-01 13:54:03.625 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 205/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.480e-03, size: 352, ETA: 2:15:56
2025-09-01 13:54:06.626 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 205/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.6, lr: 1.479e-03, size: 480, ETA: 2:15:53
2025-09-01 13:54:09.660 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 205/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.478e-03, size: 544, ETA: 2:15:49
2025-09-01 13:54:12.758 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 205/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 1.477e-03, size: 576, ETA: 2:15:46
2025-09-01 13:54:14.131 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:54:20.328 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:54:21.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:54:21.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5640
2025-09-01 13:54:21.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4690
2025-09-01 13:54:21.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3289
2025-09-01 13:54:21.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4540
2025-09-01 13:54:21.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:54:21.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:54:21.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.564
2025-09-01 13:54:21.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-09-01 13:54:21.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.329
2025-09-01 13:54:21.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.454
2025-09-01 13:54:21.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:54:21.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:54:21.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:54:21.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:54:21.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:54:21.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:54:21.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:54:21.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:54:21.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:54:22.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:54:23.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:54:24.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:54:24.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:54:25.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:54:26.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:54:26.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:54:27.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:54:28.306 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:54:28.306 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:54:28.306 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:54:28.307 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:54:28.313 | 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: 7.14 ms

2025-09-01 13:54:28.316 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:54:28.442 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:54:28.558 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch206
2025-09-01 13:54:31.512 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 206/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.003s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 1.476e-03, size: 544, ETA: 2:15:40
2025-09-01 13:54:34.459 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 206/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.8, lr: 1.476e-03, size: 288, ETA: 2:15:37
2025-09-01 13:54:37.436 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 206/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.3, cls_loss: 1.0, lr: 1.475e-03, size: 544, ETA: 2:15:33
2025-09-01 13:54:40.406 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 206/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 1.0, lr: 1.474e-03, size: 256, ETA: 2:15:29
2025-09-01 13:54:43.408 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 206/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 1.474e-03, size: 256, ETA: 2:15:25
2025-09-01 13:54:46.316 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 206/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 1.473e-03, size: 352, ETA: 2:15:22
2025-09-01 13:54:47.634 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:54:53.954 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:54:54.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:54:55.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5770
2025-09-01 13:54:55.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5016
2025-09-01 13:54:55.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3506
2025-09-01 13:54:55.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4764
2025-09-01 13:54:55.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:54:55.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:54:55.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.577
2025-09-01 13:54:55.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-09-01 13:54:55.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.351
2025-09-01 13:54:55.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.476
2025-09-01 13:54:55.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:54:55.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:54:55.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:54:55.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:54:55.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:54:55.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:54:55.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:54:55.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:54:55.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:54:56.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:54:57.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:54:58.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:54:58.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:54:59.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:55:00.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:55:01.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:55:02.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:55:02.815 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:55:02.815 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 13:55:02.815 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 13:55:02.815 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:55:02.823 | 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

2025-09-01 13:55:02.824 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:55:02.914 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:55:02.995 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch207
2025-09-01 13:55:05.960 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 207/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 8.8, iou_loss: 3.3, l1_loss: 1.3, conf_loss: 3.3, cls_loss: 0.8, lr: 1.472e-03, size: 416, ETA: 2:15:16
2025-09-01 13:55:09.058 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 207/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 1.471e-03, size: 544, ETA: 2:15:13
2025-09-01 13:55:12.102 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 207/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 1.470e-03, size: 256, ETA: 2:15:09
2025-09-01 13:55:15.063 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 207/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.9, lr: 1.470e-03, size: 448, ETA: 2:15:06
2025-09-01 13:55:18.076 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 207/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.469e-03, size: 256, ETA: 2:15:02
2025-09-01 13:55:21.042 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 207/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.9, lr: 1.468e-03, size: 512, ETA: 2:14:58
2025-09-01 13:55:22.415 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:55:28.624 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:55:29.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:55:29.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5791
2025-09-01 13:55:30.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5113
2025-09-01 13:55:30.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3539
2025-09-01 13:55:30.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4814
2025-09-01 13:55:30.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:55:30.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:55:30.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-09-01 13:55:30.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-09-01 13:55:30.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.354
2025-09-01 13:55:30.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-09-01 13:55:30.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:55:30.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:55:30.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:55:30.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:55:30.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:55:30.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:55:30.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:55:30.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:55:30.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:55:30.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:55:31.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:55:32.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:55:33.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:55:33.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:55:34.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:55:35.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:55:35.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:55:36.745 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:55:36.745 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 13:55:36.746 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 13:55:36.746 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:55:36.755 | 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: 7.02 ms

2025-09-01 13:55:36.757 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:55:36.876 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:55:36.979 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch208
2025-09-01 13:55:39.889 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 208/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.7, lr: 1.467e-03, size: 288, ETA: 2:14:53
2025-09-01 13:55:42.886 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 208/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.7, lr: 1.466e-03, size: 448, ETA: 2:14:49
2025-09-01 13:55:45.820 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 208/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 1.466e-03, size: 288, ETA: 2:14:45
2025-09-01 13:55:48.771 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 208/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 1.465e-03, size: 416, ETA: 2:14:42
2025-09-01 13:55:51.826 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 208/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 1.8, cls_loss: 0.6, lr: 1.464e-03, size: 416, ETA: 2:14:38
2025-09-01 13:55:54.761 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 208/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.7, lr: 1.464e-03, size: 512, ETA: 2:14:34
2025-09-01 13:55:56.169 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:56:02.458 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:56:02.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:56:03.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5527
2025-09-01 13:56:03.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4927
2025-09-01 13:56:03.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3402
2025-09-01 13:56:03.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4619
2025-09-01 13:56:03.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:56:03.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:56:03.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.553
2025-09-01 13:56:03.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-09-01 13:56:03.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-09-01 13:56:03.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.462
2025-09-01 13:56:03.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:56:03.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:56:03.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:56:03.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:56:03.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:56:03.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:56:03.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:56:03.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:56:03.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:56:03.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:56:04.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:56:04.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:56:05.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:56:05.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:56:05.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:56:06.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:56:06.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:56:07.154 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:56:07.154 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 13:56:07.154 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 13:56:07.155 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:56:07.161 | 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: 7.14 ms

2025-09-01 13:56:07.162 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:56:07.250 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:56:07.330 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch209
2025-09-01 13:56:10.366 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 209/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.8, lr: 1.463e-03, size: 416, ETA: 2:14:29
2025-09-01 13:56:13.308 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 209/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 2.3, cls_loss: 0.7, lr: 1.462e-03, size: 288, ETA: 2:14:26
2025-09-01 13:56:16.278 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 209/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 2.0, cls_loss: 0.7, lr: 1.461e-03, size: 512, ETA: 2:14:22
2025-09-01 13:56:19.376 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 209/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 1.460e-03, size: 320, ETA: 2:14:18
2025-09-01 13:56:22.376 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 209/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.460e-03, size: 416, ETA: 2:14:15
2025-09-01 13:56:25.389 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 209/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 24.4, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 24.4, cls_loss: 0.0, lr: 1.459e-03, size: 448, ETA: 2:14:11
2025-09-01 13:56:26.777 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:56:33.000 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:56:33.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:56:34.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5214
2025-09-01 13:56:34.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4714
2025-09-01 13:56:34.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3219
2025-09-01 13:56:34.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4382
2025-09-01 13:56:34.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:56:34.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:56:34.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 13:56:34.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-09-01 13:56:34.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.322
2025-09-01 13:56:34.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.438
2025-09-01 13:56:34.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:56:34.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:56:34.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:56:34.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:56:34.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:56:34.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:56:34.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:56:34.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:56:34.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:56:34.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:56:35.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:56:35.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:56:36.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:56:36.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:56:37.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:56:37.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:56:38.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:56:38.803 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:56:38.803 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 13:56:38.803 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 13:56:38.803 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:56:38.810 | 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: 7.05 ms

2025-09-01 13:56:38.811 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:56:38.897 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:56:38.979 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch210
2025-09-01 13:56:41.762 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 210/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.138s, data_time: 0.002s, total_loss: 9.4, iou_loss: 3.2, l1_loss: 1.0, conf_loss: 4.4, cls_loss: 0.9, lr: 1.458e-03, size: 448, ETA: 2:14:06
2025-09-01 13:56:44.771 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 210/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 1.1, lr: 1.457e-03, size: 448, ETA: 2:14:02
2025-09-01 13:56:47.773 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 210/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 3.2, cls_loss: 0.7, lr: 1.456e-03, size: 544, ETA: 2:13:58
2025-09-01 13:56:50.992 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 210/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.9, lr: 1.456e-03, size: 288, ETA: 2:13:55
2025-09-01 13:56:54.040 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 210/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 1.455e-03, size: 544, ETA: 2:13:52
2025-09-01 13:56:57.056 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 210/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, 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: 1.454e-03, size: 320, ETA: 2:13:48
2025-09-01 13:56:58.431 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:57:04.722 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:57:05.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:57:06.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5738
2025-09-01 13:57:06.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4902
2025-09-01 13:57:06.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3280
2025-09-01 13:57:06.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4640
2025-09-01 13:57:06.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:57:06.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:57:06.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-09-01 13:57:06.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-09-01 13:57:06.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.328
2025-09-01 13:57:06.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.464
2025-09-01 13:57:06.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:57:06.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:57:06.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:57:06.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:57:06.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:57:06.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:57:06.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:57:06.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:57:06.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:57:07.786 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:57:08.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:57:09.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:57:10.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:57:11.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:57:12.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:57:13.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:57:14.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:57:15.316 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:57:15.316 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 13:57:15.316 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 13:57:15.317 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:57:15.324 | 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

2025-09-01 13:57:15.325 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:57:15.448 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:57:15.524 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch211
2025-09-01 13:57:18.381 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 211/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 9.1, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 4.2, cls_loss: 0.8, lr: 1.453e-03, size: 480, ETA: 2:13:43
2025-09-01 13:57:21.415 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 211/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 1.453e-03, size: 544, ETA: 2:13:39
2025-09-01 13:57:24.388 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 211/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.452e-03, size: 480, ETA: 2:13:35
2025-09-01 13:57:27.422 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 211/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 1.451e-03, size: 544, ETA: 2:13:32
2025-09-01 13:57:30.479 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 211/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 1.450e-03, size: 512, ETA: 2:13:28
2025-09-01 13:57:33.550 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 211/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, 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: 1.450e-03, size: 544, ETA: 2:13:25
2025-09-01 13:57:34.943 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:57:41.210 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:57:42.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:57:43.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5765
2025-09-01 13:57:43.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4844
2025-09-01 13:57:44.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3505
2025-09-01 13:57:44.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4704
2025-09-01 13:57:44.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:57:44.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:57:44.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-09-01 13:57:44.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-09-01 13:57:44.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.350
2025-09-01 13:57:44.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.470
2025-09-01 13:57:44.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:57:44.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:57:44.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:57:44.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:57:44.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:57:44.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:57:44.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:57:44.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:57:44.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:57:45.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:57:46.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:57:47.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:57:49.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:57:50.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:57:51.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:57:53.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:57:54.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:57:55.776 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:57:55.776 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 13:57:55.777 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 13:57:55.777 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:57:55.784 | 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

2025-09-01 13:57:55.785 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:57:55.918 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:57:55.996 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch212
2025-09-01 13:57:58.688 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 212/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.133s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.6, lr: 1.449e-03, size: 352, ETA: 2:13:19
2025-09-01 13:58:01.762 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 212/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.8, lr: 1.448e-03, size: 576, ETA: 2:13:16
2025-09-01 13:58:04.853 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 212/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.447e-03, size: 448, ETA: 2:13:12
2025-09-01 13:58:07.991 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 212/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.7, lr: 1.446e-03, size: 512, ETA: 2:13:09
2025-09-01 13:58:11.047 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 212/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, 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: 1.446e-03, size: 320, ETA: 2:13:05
2025-09-01 13:58:14.158 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 212/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.445e-03, size: 512, ETA: 2:13:02
2025-09-01 13:58:15.578 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:58:21.998 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:58:22.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:58:23.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5893
2025-09-01 13:58:23.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5260
2025-09-01 13:58:23.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3560
2025-09-01 13:58:23.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4904
2025-09-01 13:58:23.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:58:23.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:58:23.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-09-01 13:58:23.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-09-01 13:58:23.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-09-01 13:58:23.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-09-01 13:58:23.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:58:23.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:58:23.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:58:23.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:58:23.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:58:23.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:58:23.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:58:23.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:58:23.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:58:24.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:58:25.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:58:25.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:58:26.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:58:27.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:58:28.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:58:28.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:58:29.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:58:30.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:58:30.225 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 13:58:30.226 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 13:58:30.226 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:58:30.233 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.92 ms, Average inference time: 7.04 ms

2025-09-01 13:58:30.234 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:58:30.314 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:58:30.399 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch213
2025-09-01 13:58:33.337 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 213/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.444e-03, size: 320, ETA: 2:12:57
2025-09-01 13:58:36.422 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 213/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.8, lr: 1.443e-03, size: 352, ETA: 2:12:53
2025-09-01 13:58:39.443 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 213/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 1.442e-03, size: 480, ETA: 2:12:50
2025-09-01 13:58:42.430 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 213/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.6, lr: 1.442e-03, size: 480, ETA: 2:12:46
2025-09-01 13:58:45.543 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 213/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 1.441e-03, size: 512, ETA: 2:12:43
2025-09-01 13:58:48.560 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 213/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 1.440e-03, size: 416, ETA: 2:12:39
2025-09-01 13:58:49.943 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:58:56.210 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:58:57.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:58:57.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5558
2025-09-01 13:58:57.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4982
2025-09-01 13:58:57.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3077
2025-09-01 13:58:57.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4539
2025-09-01 13:58:57.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:58:57.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:58:57.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.556
2025-09-01 13:58:57.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-09-01 13:58:57.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.308
2025-09-01 13:58:57.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.454
2025-09-01 13:58:57.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:58:57.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:58:57.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:58:57.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:58:57.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:58:57.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:58:57.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:58:57.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:58:57.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:58:58.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:58:59.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:58:59.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:59:00.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:59:01.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:59:01.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:59:02.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:59:03.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:59:04.004 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:59:04.004 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 13:59:04.004 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 13:59:04.004 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:59:04.012 | 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

2025-09-01 13:59:04.013 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:59:04.099 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:59:04.222 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch214
2025-09-01 13:59:07.145 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 214/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 1.439e-03, size: 512, ETA: 2:12:34
2025-09-01 13:59:10.097 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 214/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 1.438e-03, size: 512, ETA: 2:12:30
2025-09-01 13:59:13.151 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 214/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.9, lr: 1.438e-03, size: 480, ETA: 2:12:27
2025-09-01 13:59:16.263 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 214/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 9.6, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 5.0, cls_loss: 1.3, lr: 1.437e-03, size: 448, ETA: 2:12:23
2025-09-01 13:59:19.427 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 214/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 1.436e-03, size: 416, ETA: 2:12:20
2025-09-01 13:59:22.536 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 214/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.4, l1_loss: 1.4, conf_loss: 3.5, cls_loss: 0.7, lr: 1.436e-03, size: 512, ETA: 2:12:17
2025-09-01 13:59:23.970 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:59:30.149 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 13:59:30.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 13:59:31.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5775
2025-09-01 13:59:31.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4982
2025-09-01 13:59:31.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3562
2025-09-01 13:59:31.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4773
2025-09-01 13:59:31.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 13:59:31.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 13:59:31.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-09-01 13:59:31.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.498
2025-09-01 13:59:31.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-09-01 13:59:31.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.477
2025-09-01 13:59:31.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 13:59:31.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 13:59:31.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 13:59:31.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 13:59:31.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 13:59:31.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 13:59:31.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 13:59:31.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 13:59:31.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 13:59:32.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 13:59:32.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 13:59:33.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 13:59:33.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 13:59:34.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 13:59:35.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 13:59:35.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 13:59:36.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 13:59:36.862 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 13:59:36.862 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 13:59:36.862 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 13:59:36.862 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 13:59:36.872 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.94 ms, Average inference time: 7.25 ms

2025-09-01 13:59:36.873 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:59:36.952 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 13:59:37.036 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch215
2025-09-01 13:59:39.941 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 215/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 2.5, cls_loss: 0.7, lr: 1.435e-03, size: 288, ETA: 2:12:11
2025-09-01 13:59:42.867 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 215/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.6, lr: 1.434e-03, size: 320, ETA: 2:12:08
2025-09-01 13:59:45.900 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 215/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 2.3, cls_loss: 1.0, lr: 1.433e-03, size: 448, ETA: 2:12:04
2025-09-01 13:59:48.844 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 215/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 1.432e-03, size: 416, ETA: 2:12:00
2025-09-01 13:59:51.917 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 215/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 1.1, lr: 1.432e-03, size: 576, ETA: 2:11:57
2025-09-01 13:59:55.055 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 215/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 1.431e-03, size: 480, ETA: 2:11:54
2025-09-01 13:59:56.431 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:00:02.958 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:00:04.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:00:05.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5595
2025-09-01 14:00:05.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4950
2025-09-01 14:00:05.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3678
2025-09-01 14:00:05.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4741
2025-09-01 14:00:05.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:00:05.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:00:05.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.560
2025-09-01 14:00:05.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-09-01 14:00:05.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-09-01 14:00:05.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.474
2025-09-01 14:00:05.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:00:05.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:00:05.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:00:05.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:00:05.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:00:05.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:00:05.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:00:05.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:00:05.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:00:06.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:00:07.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:00:08.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:00:09.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:00:10.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:00:11.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:00:12.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:00:13.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:00:15.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:00:15.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:00:15.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 14:00:15.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:00:15.034 | 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

2025-09-01 14:00:15.035 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:00:15.113 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:00:15.198 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch216
2025-09-01 14:00:18.137 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 216/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.8, cls_loss: 0.8, lr: 1.430e-03, size: 448, ETA: 2:11:48
2025-09-01 14:00:21.249 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 216/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.7, lr: 1.429e-03, size: 512, ETA: 2:11:45
2025-09-01 14:00:24.321 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 216/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.6, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.9, cls_loss: 0.8, lr: 1.428e-03, size: 448, ETA: 2:11:42
2025-09-01 14:00:27.368 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 216/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.7, lr: 1.428e-03, size: 416, ETA: 2:11:38
2025-09-01 14:00:30.313 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 216/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.9, l1_loss: 1.5, conf_loss: 3.0, cls_loss: 0.8, lr: 1.427e-03, size: 576, ETA: 2:11:34
2025-09-01 14:00:33.285 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 216/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, 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: 1.426e-03, size: 384, ETA: 2:11:31
2025-09-01 14:00:34.636 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:00:40.846 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:00:41.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:00:42.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5794
2025-09-01 14:00:42.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5072
2025-09-01 14:00:42.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3566
2025-09-01 14:00:42.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4811
2025-09-01 14:00:42.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:00:42.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:00:42.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-09-01 14:00:42.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-09-01 14:00:42.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.357
2025-09-01 14:00:42.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-09-01 14:00:42.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:00:42.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:00:42.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:00:42.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:00:42.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:00:42.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:00:42.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:00:42.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:00:42.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:00:43.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:00:44.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:00:45.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:00:45.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:00:46.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:00:47.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:00:48.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:00:49.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:00:49.971 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:00:49.972 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:00:49.972 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:00:49.972 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:00:49.979 | 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

2025-09-01 14:00:49.981 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:00:50.064 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:00:50.145 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch217
2025-09-01 14:00:53.019 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 217/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 4.0, cls_loss: 0.7, lr: 1.425e-03, size: 416, ETA: 2:11:25
2025-09-01 14:00:55.965 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 217/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.7, lr: 1.424e-03, size: 416, ETA: 2:11:22
2025-09-01 14:00:58.971 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 217/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.424e-03, size: 480, ETA: 2:11:18
2025-09-01 14:01:01.999 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 217/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.8, lr: 1.423e-03, size: 352, ETA: 2:11:14
2025-09-01 14:01:04.917 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 217/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.3, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 2.9, cls_loss: 1.1, lr: 1.422e-03, size: 416, ETA: 2:11:11
2025-09-01 14:01:07.914 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 217/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 8.4, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 3.3, cls_loss: 0.8, lr: 1.421e-03, size: 416, ETA: 2:11:07
2025-09-01 14:01:09.276 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:01:15.617 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:01:16.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:01:17.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5736
2025-09-01 14:01:17.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5144
2025-09-01 14:01:17.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3650
2025-09-01 14:01:17.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4843
2025-09-01 14:01:17.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:01:17.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:01:17.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-09-01 14:01:17.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.514
2025-09-01 14:01:17.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-09-01 14:01:17.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-09-01 14:01:17.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:01:17.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:01:17.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:01:17.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:01:17.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:01:17.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:01:17.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:01:17.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:01:17.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:01:18.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:01:19.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:01:19.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:01:20.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:01:21.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:01:22.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:01:22.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:01:23.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:01:24.528 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:01:24.529 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:01:24.529 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:01:24.529 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:01:24.537 | 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

2025-09-01 14:01:24.543 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:01:24.664 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:01:24.739 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch218
2025-09-01 14:01:27.685 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 218/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, 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: 1.420e-03, size: 576, ETA: 2:11:02
2025-09-01 14:01:30.791 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 218/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.420e-03, size: 512, ETA: 2:10:58
2025-09-01 14:01:33.736 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 218/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 1.419e-03, size: 320, ETA: 2:10:55
2025-09-01 14:01:36.576 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 218/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 0.7, lr: 1.418e-03, size: 256, ETA: 2:10:51
2025-09-01 14:01:39.455 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 218/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.7, lr: 1.417e-03, size: 256, ETA: 2:10:47
2025-09-01 14:01:42.390 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 218/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.0, cls_loss: 0.7, lr: 1.417e-03, size: 544, ETA: 2:10:43
2025-09-01 14:01:43.805 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:01:49.848 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:01:50.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:01:50.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5543
2025-09-01 14:01:50.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4898
2025-09-01 14:01:50.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3733
2025-09-01 14:01:50.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4725
2025-09-01 14:01:50.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:01:50.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:01:50.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.554
2025-09-01 14:01:50.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-09-01 14:01:50.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.373
2025-09-01 14:01:50.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.472
2025-09-01 14:01:50.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:01:50.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:01:50.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:01:50.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:01:50.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:01:50.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:01:50.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:01:50.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:01:50.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:01:51.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:01:51.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:01:51.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:01:52.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:01:52.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:01:52.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:01:53.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:01:53.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:01:53.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:01:53.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:01:53.754 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 14:01:53.754 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:01:53.760 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.82 ms, Average inference time: 7.04 ms

2025-09-01 14:01:53.764 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:01:53.855 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:01:53.933 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch219
2025-09-01 14:01:56.800 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 219/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.142s, 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: 1.416e-03, size: 288, ETA: 2:10:38
2025-09-01 14:01:59.851 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 219/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.6, lr: 1.415e-03, size: 256, ETA: 2:10:35
2025-09-01 14:02:02.815 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 219/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.4, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 2.3, cls_loss: 0.8, lr: 1.414e-03, size: 352, ETA: 2:10:31
2025-09-01 14:02:05.842 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 219/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.413e-03, size: 256, ETA: 2:10:28
2025-09-01 14:02:08.942 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 219/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, 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: 1.413e-03, size: 320, ETA: 2:10:24
2025-09-01 14:02:12.101 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 219/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 8.9, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 3.9, cls_loss: 1.2, lr: 1.412e-03, size: 544, ETA: 2:10:21
2025-09-01 14:02:13.491 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:02:19.741 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:02:20.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:02:21.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5369
2025-09-01 14:02:21.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4811
2025-09-01 14:02:21.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2555
2025-09-01 14:02:21.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4245
2025-09-01 14:02:21.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:02:21.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:02:21.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-09-01 14:02:21.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-09-01 14:02:21.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.255
2025-09-01 14:02:21.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.424
2025-09-01 14:02:21.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:02:21.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:02:21.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:02:21.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:02:21.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:02:21.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:02:21.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:02:21.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:02:21.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:02:22.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:02:22.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:02:23.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:02:24.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:02:24.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:02:25.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:02:26.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:02:26.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:02:27.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:02:27.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 14:02:27.664 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.42
2025-09-01 14:02:27.665 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:02:27.671 | 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

2025-09-01 14:02:27.673 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:02:27.764 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:02:27.847 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch220
2025-09-01 14:02:30.691 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 220/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.7, lr: 1.411e-03, size: 480, ETA: 2:10:15
2025-09-01 14:02:33.743 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 220/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.7, lr: 1.410e-03, size: 256, ETA: 2:10:12
2025-09-01 14:02:36.721 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 220/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.8, lr: 1.409e-03, size: 352, ETA: 2:10:08
2025-09-01 14:02:39.785 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 220/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.8, lr: 1.409e-03, size: 416, ETA: 2:10:05
2025-09-01 14:02:42.748 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 220/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.6, cls_loss: 0.8, lr: 1.408e-03, size: 544, ETA: 2:10:01
2025-09-01 14:02:45.776 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 220/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.6, lr: 1.407e-03, size: 544, ETA: 2:09:58
2025-09-01 14:02:47.124 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:02:53.433 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:02:54.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:02:54.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5877
2025-09-01 14:02:54.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4824
2025-09-01 14:02:54.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3564
2025-09-01 14:02:54.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4755
2025-09-01 14:02:54.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:02:54.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:02:54.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-09-01 14:02:54.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-09-01 14:02:54.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-09-01 14:02:54.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.475
2025-09-01 14:02:54.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:02:54.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:02:54.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:02:54.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:02:54.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:02:54.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:02:54.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:02:54.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:02:54.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:02:55.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:02:55.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:02:56.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:02:56.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:02:57.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:02:57.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:02:58.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:02:58.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:02:59.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:02:59.113 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:02:59.113 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:02:59.113 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:02:59.119 | 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: 7.13 ms

2025-09-01 14:02:59.120 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:02:59.211 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:02:59.290 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch221
2025-09-01 14:03:02.138 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 221/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.141s, 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: 1.406e-03, size: 448, ETA: 2:09:52
2025-09-01 14:03:05.260 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 221/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 1.405e-03, size: 384, ETA: 2:09:49
2025-09-01 14:03:08.240 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 221/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 1.0, lr: 1.405e-03, size: 480, ETA: 2:09:45
2025-09-01 14:03:11.246 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 221/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.404e-03, size: 416, ETA: 2:09:42
2025-09-01 14:03:14.202 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 221/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 1.403e-03, size: 352, ETA: 2:09:38
2025-09-01 14:03:17.113 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 221/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 1.402e-03, size: 416, ETA: 2:09:35
2025-09-01 14:03:18.538 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:03:24.665 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:03:25.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:03:26.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5827
2025-09-01 14:03:26.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5114
2025-09-01 14:03:26.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3900
2025-09-01 14:03:26.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4947
2025-09-01 14:03:26.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:03:26.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:03:26.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.583
2025-09-01 14:03:26.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-09-01 14:03:26.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-09-01 14:03:26.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-09-01 14:03:26.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:03:26.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:03:26.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:03:26.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:03:26.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:03:26.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:03:26.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:03:26.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:03:26.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:03:27.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:03:27.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:03:28.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:03:29.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:03:29.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:03:30.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:03:31.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:03:32.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:03:32.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:03:32.866 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:03:32.866 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:03:32.867 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:03:32.879 | 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

2025-09-01 14:03:32.880 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:03:33.050 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:03:33.134 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch222
2025-09-01 14:03:36.009 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 222/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 8.5, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 0.7, lr: 1.401e-03, size: 480, ETA: 2:09:29
2025-09-01 14:03:39.094 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 222/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.400e-03, size: 256, ETA: 2:09:26
2025-09-01 14:03:42.053 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 222/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 1.1, lr: 1.400e-03, size: 448, ETA: 2:09:22
2025-09-01 14:03:45.027 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 222/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, 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: 1.399e-03, size: 288, ETA: 2:09:19
2025-09-01 14:03:48.082 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 222/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.398e-03, size: 448, ETA: 2:09:15
2025-09-01 14:03:51.133 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 222/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 1.397e-03, size: 448, ETA: 2:09:12
2025-09-01 14:03:52.486 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:03:58.757 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:03:59.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:04:00.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5894
2025-09-01 14:04:00.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5065
2025-09-01 14:04:00.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3788
2025-09-01 14:04:00.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4916
2025-09-01 14:04:00.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:04:00.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:04:00.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-09-01 14:04:00.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-09-01 14:04:00.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-09-01 14:04:00.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-09-01 14:04:00.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:04:00.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:04:00.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:04:00.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:04:00.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:04:00.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:04:00.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:04:00.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:04:00.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:04:01.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:04:02.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:04:03.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:04:04.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:04:04.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:04:05.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:04:06.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:04:07.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:04:08.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:04:08.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:04:08.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:04:08.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:04:08.635 | 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

2025-09-01 14:04:08.636 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:04:08.717 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:04:08.800 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch223
2025-09-01 14:04:11.673 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 223/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.142s, 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: 1.396e-03, size: 480, ETA: 2:09:06
2025-09-01 14:04:14.663 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 223/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 1.396e-03, size: 416, ETA: 2:09:03
2025-09-01 14:04:17.754 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 223/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 1.5, conf_loss: 2.4, cls_loss: 0.7, lr: 1.395e-03, size: 384, ETA: 2:08:59
2025-09-01 14:04:20.778 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 223/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.1, l1_loss: 1.2, conf_loss: 3.2, cls_loss: 0.6, lr: 1.394e-03, size: 512, ETA: 2:08:56
2025-09-01 14:04:23.872 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 223/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.7, lr: 1.393e-03, size: 480, ETA: 2:08:53
2025-09-01 14:04:26.915 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 223/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.5, lr: 1.393e-03, size: 352, ETA: 2:08:49
2025-09-01 14:04:28.293 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:04:34.598 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:04:35.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:04:35.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5360
2025-09-01 14:04:35.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4902
2025-09-01 14:04:35.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3188
2025-09-01 14:04:35.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4484
2025-09-01 14:04:35.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:04:35.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:04:35.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-09-01 14:04:35.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-09-01 14:04:35.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.319
2025-09-01 14:04:35.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.448
2025-09-01 14:04:35.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:04:35.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:04:35.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:04:35.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:04:35.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:04:35.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:04:35.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:04:35.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:04:35.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:04:36.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:04:36.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:04:36.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:04:37.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:04:37.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:04:38.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:04:38.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:04:39.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:04:39.516 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:04:39.517 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:04:39.517 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 14:04:39.517 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:04:39.524 | 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

2025-09-01 14:04:39.525 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:04:39.653 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:04:39.729 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch224
2025-09-01 14:04:42.634 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 224/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.392e-03, size: 576, ETA: 2:08:44
2025-09-01 14:04:45.761 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 224/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, 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: 1.391e-03, size: 544, ETA: 2:08:40
2025-09-01 14:04:48.709 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 224/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.8, lr: 1.390e-03, size: 448, ETA: 2:08:37
2025-09-01 14:04:51.705 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 224/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.8, lr: 1.389e-03, size: 320, ETA: 2:08:33
2025-09-01 14:04:54.668 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 224/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 1.389e-03, size: 384, ETA: 2:08:30
2025-09-01 14:04:57.656 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 224/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 1.0, lr: 1.388e-03, size: 448, ETA: 2:08:26
2025-09-01 14:04:59.138 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:05:05.405 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:05:06.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:05:06.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5613
2025-09-01 14:05:06.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5139
2025-09-01 14:05:06.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3058
2025-09-01 14:05:06.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4603
2025-09-01 14:05:06.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:05:06.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:05:06.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.561
2025-09-01 14:05:06.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.514
2025-09-01 14:05:06.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.306
2025-09-01 14:05:06.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.460
2025-09-01 14:05:06.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:05:06.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:05:06.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:05:06.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:05:06.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:05:06.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:05:06.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:05:06.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:05:06.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:05:07.326 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:05:07.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:05:08.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:05:09.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:05:09.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:05:10.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:05:10.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:05:11.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:05:11.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:05:11.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:05:11.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 14:05:11.893 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:05:11.900 | 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

2025-09-01 14:05:11.902 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:05:11.983 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:05:12.065 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch225
2025-09-01 14:05:14.940 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 225/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.387e-03, size: 448, ETA: 2:08:21
2025-09-01 14:05:18.004 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 225/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 1.0, lr: 1.386e-03, size: 576, ETA: 2:08:18
2025-09-01 14:05:21.029 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 225/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 9.7, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 4.6, cls_loss: 0.8, lr: 1.385e-03, size: 512, ETA: 2:08:14
2025-09-01 14:05:24.100 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 225/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.8, lr: 1.385e-03, size: 544, ETA: 2:08:11
2025-09-01 14:05:27.140 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 225/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.4, conf_loss: 2.3, cls_loss: 0.8, lr: 1.384e-03, size: 544, ETA: 2:08:07
2025-09-01 14:05:30.154 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 225/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.9, lr: 1.383e-03, size: 352, ETA: 2:08:04
2025-09-01 14:05:31.500 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:05:37.780 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:05:39.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:05:40.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5696
2025-09-01 14:05:40.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4931
2025-09-01 14:05:40.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3616
2025-09-01 14:05:40.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4748
2025-09-01 14:05:40.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:05:40.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:05:40.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.570
2025-09-01 14:05:40.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-09-01 14:05:40.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-09-01 14:05:40.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.475
2025-09-01 14:05:40.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:05:40.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:05:40.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:05:40.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:05:40.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:05:40.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:05:40.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:05:40.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:05:40.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:05:41.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:05:42.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:05:43.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:05:44.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:05:45.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:05:47.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:05:48.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:05:49.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:05:50.564 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:05:50.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:05:50.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 14:05:50.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:05:50.572 | 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

2025-09-01 14:05:50.573 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:05:50.720 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:05:50.825 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch226
2025-09-01 14:05:53.834 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 226/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.5, lr: 1.382e-03, size: 480, ETA: 2:07:59
2025-09-01 14:05:56.975 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 226/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 8.3, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.4, cls_loss: 0.8, lr: 1.381e-03, size: 320, ETA: 2:07:55
2025-09-01 14:06:00.060 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 226/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.9, cls_loss: 0.8, lr: 1.380e-03, size: 512, ETA: 2:07:52
2025-09-01 14:06:03.125 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 226/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.1, cls_loss: 0.7, lr: 1.380e-03, size: 352, ETA: 2:07:48
2025-09-01 14:06:06.226 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 226/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.7, lr: 1.379e-03, size: 512, ETA: 2:07:45
2025-09-01 14:06:09.258 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 226/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.6, lr: 1.378e-03, size: 448, ETA: 2:07:42
2025-09-01 14:06:10.588 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:06:16.679 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:06:17.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:06:17.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5816
2025-09-01 14:06:17.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5157
2025-09-01 14:06:17.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3386
2025-09-01 14:06:17.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4786
2025-09-01 14:06:17.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:06:17.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:06:17.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-09-01 14:06:17.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-09-01 14:06:17.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.339
2025-09-01 14:06:17.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.479
2025-09-01 14:06:17.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:06:17.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:06:17.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:06:17.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:06:17.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:06:17.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:06:17.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:06:17.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:06:17.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:06:18.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:06:18.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:06:19.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:06:19.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:06:20.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:06:20.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:06:21.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:06:21.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:06:22.468 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:06:22.468 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:06:22.468 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:06:22.469 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:06:22.475 | 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

2025-09-01 14:06:22.482 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:06:22.569 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:06:22.649 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch227
2025-09-01 14:06:25.511 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 227/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.377e-03, size: 320, ETA: 2:07:36
2025-09-01 14:06:28.467 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 227/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 9.4, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 4.2, cls_loss: 0.9, lr: 1.376e-03, size: 576, ETA: 2:07:33
2025-09-01 14:06:31.499 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 227/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 1.376e-03, size: 320, ETA: 2:07:29
2025-09-01 14:06:34.459 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 227/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 0.8, lr: 1.375e-03, size: 288, ETA: 2:07:26
2025-09-01 14:06:37.460 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 227/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 1.374e-03, size: 256, ETA: 2:07:22
2025-09-01 14:06:40.418 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 227/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.003s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 1.1, lr: 1.373e-03, size: 352, ETA: 2:07:19
2025-09-01 14:06:41.786 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:06:48.132 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:06:49.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:06:49.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5970
2025-09-01 14:06:49.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5213
2025-09-01 14:06:49.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3974
2025-09-01 14:06:49.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5052
2025-09-01 14:06:49.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:06:49.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:06:49.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.597
2025-09-01 14:06:49.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 14:06:49.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-09-01 14:06:49.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-09-01 14:06:49.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:06:49.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:06:49.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:06:49.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:06:49.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:06:49.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:06:49.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:06:49.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:06:49.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:06:50.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:06:51.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:06:52.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:06:53.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:06:54.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:06:55.100 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:06:55.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:06:56.772 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:06:57.671 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:06:57.672 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:06:57.672 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 14:06:57.672 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:06:57.679 | 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: 7.05 ms

2025-09-01 14:06:57.681 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:06:57.768 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:06:57.848 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch228
2025-09-01 14:07:00.901 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 228/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.5, lr: 1.372e-03, size: 512, ETA: 2:07:14
2025-09-01 14:07:04.092 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 228/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 1.371e-03, size: 384, ETA: 2:07:10
2025-09-01 14:07:07.100 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 228/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 2.9, cls_loss: 0.7, lr: 1.371e-03, size: 480, ETA: 2:07:07
2025-09-01 14:07:10.187 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 228/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, 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: 1.370e-03, size: 544, ETA: 2:07:03
2025-09-01 14:07:13.184 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 228/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.369e-03, size: 288, ETA: 2:07:00
2025-09-01 14:07:16.085 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 228/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.8, lr: 1.368e-03, size: 352, ETA: 2:06:56
2025-09-01 14:07:17.430 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:07:23.816 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:07:24.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:07:25.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5883
2025-09-01 14:07:25.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5205
2025-09-01 14:07:25.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3563
2025-09-01 14:07:25.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4884
2025-09-01 14:07:25.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:07:25.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:07:25.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-09-01 14:07:25.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 14:07:25.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-09-01 14:07:25.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-09-01 14:07:25.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:07:25.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:07:25.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:07:25.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:07:25.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:07:25.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:07:25.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:07:25.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:07:25.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:07:26.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:07:27.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:07:28.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:07:29.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:07:30.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:07:31.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:07:32.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:07:33.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:07:34.044 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:07:34.044 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:07:34.044 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:07:34.044 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:07:34.051 | 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: 7.20 ms

2025-09-01 14:07:34.054 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:07:34.181 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:07:34.260 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch229
2025-09-01 14:07:37.133 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 229/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 0.7, cls_loss: 0.6, lr: 1.367e-03, size: 256, ETA: 2:06:51
2025-09-01 14:07:40.012 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 229/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.142s, 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: 1.367e-03, size: 320, ETA: 2:06:47
2025-09-01 14:07:43.095 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 229/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, 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: 1.366e-03, size: 576, ETA: 2:06:44
2025-09-01 14:07:46.208 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 229/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.8, lr: 1.365e-03, size: 448, ETA: 2:06:40
2025-09-01 14:07:49.371 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 229/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 9.7, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 4.9, cls_loss: 0.8, lr: 1.364e-03, size: 352, ETA: 2:06:37
2025-09-01 14:07:52.456 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 229/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.006s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 1.364e-03, size: 448, ETA: 2:06:34
2025-09-01 14:07:53.820 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:08:00.081 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:08:00.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:08:01.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5806
2025-09-01 14:08:01.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5023
2025-09-01 14:08:01.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3808
2025-09-01 14:08:01.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4879
2025-09-01 14:08:01.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:08:01.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:08:01.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.581
2025-09-01 14:08:01.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-09-01 14:08:01.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-09-01 14:08:01.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-09-01 14:08:01.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:08:01.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:08:01.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:08:01.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:08:01.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:08:01.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:08:01.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:08:01.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:08:01.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:08:02.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:08:03.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:08:03.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:08:04.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:08:05.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:08:05.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:08:06.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:08:07.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:08:08.077 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:08:08.078 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:08:08.078 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:08:08.078 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:08:08.085 | 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: 7.14 ms

2025-09-01 14:08:08.086 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:08:08.166 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:08:08.250 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch230
2025-09-01 14:08:11.231 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 230/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.6, cls_loss: 0.7, lr: 1.362e-03, size: 448, ETA: 2:06:29
2025-09-01 14:08:14.352 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 230/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.8, lr: 1.362e-03, size: 256, ETA: 2:06:25
2025-09-01 14:08:17.304 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 230/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.6, lr: 1.361e-03, size: 576, ETA: 2:06:22
2025-09-01 14:08:20.394 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 230/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 10.0, iou_loss: 3.1, l1_loss: 1.6, conf_loss: 4.1, cls_loss: 1.3, lr: 1.360e-03, size: 448, ETA: 2:06:18
2025-09-01 14:08:23.475 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 230/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.8, iou_loss: 2.9, l1_loss: 2.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.359e-03, size: 512, ETA: 2:06:15
2025-09-01 14:08:26.481 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 230/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, 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: 1.359e-03, size: 384, ETA: 2:06:12
2025-09-01 14:08:27.822 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:08:34.220 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:08:35.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:08:35.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5974
2025-09-01 14:08:35.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5210
2025-09-01 14:08:35.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3887
2025-09-01 14:08:35.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5024
2025-09-01 14:08:35.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:08:35.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:08:35.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.597
2025-09-01 14:08:35.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 14:08:35.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-09-01 14:08:35.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 14:08:35.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:08:35.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:08:35.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:08:35.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:08:35.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:08:35.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:08:35.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:08:35.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:08:35.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:08:36.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:08:37.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:08:38.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:08:39.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:08:39.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:08:40.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:08:41.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:08:42.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:08:42.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:08:42.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:08:42.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 14:08:42.839 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:08:42.847 | 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: 6.99 ms

2025-09-01 14:08:42.852 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:08:42.939 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:08:43.063 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch231
2025-09-01 14:08:45.896 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 231/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 1.358e-03, size: 448, ETA: 2:06:06
2025-09-01 14:08:48.969 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 231/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.6, lr: 1.357e-03, size: 544, ETA: 2:06:03
2025-09-01 14:08:52.123 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 231/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, 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: 1.356e-03, size: 480, ETA: 2:06:00
2025-09-01 14:08:55.080 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 231/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.8, lr: 1.355e-03, size: 320, ETA: 2:05:56
2025-09-01 14:08:58.017 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 231/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.7, lr: 1.355e-03, size: 416, ETA: 2:05:52
2025-09-01 14:09:01.175 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 231/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, 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: 1.354e-03, size: 320, ETA: 2:05:49
2025-09-01 14:09:02.573 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:09:08.618 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:09:10.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:09:11.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5740
2025-09-01 14:09:11.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5105
2025-09-01 14:09:11.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3494
2025-09-01 14:09:11.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4780
2025-09-01 14:09:11.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:09:11.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:09:11.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-09-01 14:09:11.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-09-01 14:09:11.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.349
2025-09-01 14:09:11.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-09-01 14:09:11.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:09:11.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:09:11.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:09:11.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:09:11.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:09:11.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:09:11.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:09:11.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:09:11.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:09:12.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:09:13.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:09:14.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:09:16.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:09:17.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:09:18.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:09:19.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:09:21.110 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:09:22.336 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:09:22.336 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:09:22.336 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:09:22.336 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:09:22.344 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.23 ms, Average NMS time: 1.01 ms, Average inference time: 7.23 ms

2025-09-01 14:09:22.349 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:09:22.429 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:09:22.513 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch232
2025-09-01 14:09:25.401 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 232/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 1.353e-03, size: 480, ETA: 2:05:44
2025-09-01 14:09:28.438 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 232/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 1.352e-03, size: 576, ETA: 2:05:41
2025-09-01 14:09:31.603 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 232/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 1.351e-03, size: 352, ETA: 2:05:37
2025-09-01 14:09:34.539 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 232/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, 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: 1.350e-03, size: 288, ETA: 2:05:34
2025-09-01 14:09:37.534 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 232/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.7, lr: 1.350e-03, size: 320, ETA: 2:05:30
2025-09-01 14:09:40.621 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 232/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 8.1, iou_loss: 3.3, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.7, lr: 1.349e-03, size: 256, ETA: 2:05:27
2025-09-01 14:09:41.976 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:09:48.313 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:09:49.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:09:49.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5952
2025-09-01 14:09:49.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5172
2025-09-01 14:09:50.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3645
2025-09-01 14:09:50.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4923
2025-09-01 14:09:50.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:09:50.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:09:50.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.595
2025-09-01 14:09:50.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 14:09:50.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-09-01 14:09:50.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-09-01 14:09:50.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:09:50.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:09:50.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:09:50.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:09:50.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:09:50.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:09:50.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:09:50.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:09:50.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:09:50.809 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:09:51.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:09:52.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:09:53.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:09:53.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:09:54.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:09:55.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:09:56.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:09:57.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:09:57.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:09:57.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:09:57.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:09:57.037 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.90 ms, Average inference time: 7.04 ms

2025-09-01 14:09:57.039 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:09:57.122 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:09:57.203 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch233
2025-09-01 14:10:00.084 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 233/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 3.3, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 0.9, cls_loss: 0.5, lr: 1.348e-03, size: 320, ETA: 2:05:22
2025-09-01 14:10:03.189 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 233/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 1.4, lr: 1.347e-03, size: 352, ETA: 2:05:18
2025-09-01 14:10:06.353 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 233/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.346e-03, size: 576, ETA: 2:05:15
2025-09-01 14:10:09.421 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 233/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.6, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.5, lr: 1.346e-03, size: 416, ETA: 2:05:12
2025-09-01 14:10:12.534 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 233/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, 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: 1.345e-03, size: 384, ETA: 2:05:08
2025-09-01 14:10:15.564 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 233/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, 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: 1.344e-03, size: 352, ETA: 2:05:05
2025-09-01 14:10:16.944 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:10:23.213 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:10:24.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:10:24.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5726
2025-09-01 14:10:24.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5037
2025-09-01 14:10:24.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3875
2025-09-01 14:10:24.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4880
2025-09-01 14:10:24.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:10:24.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:10:24.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.573
2025-09-01 14:10:24.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-09-01 14:10:24.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-09-01 14:10:24.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-09-01 14:10:24.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:10:24.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:10:24.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:10:24.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:10:24.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:10:24.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:10:24.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:10:24.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:10:24.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:10:25.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:10:25.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:10:26.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:10:27.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:10:27.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:10:28.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:10:29.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:10:29.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:10:30.340 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:10:30.340 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:10:30.340 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:10:30.340 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:10:30.348 | 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

2025-09-01 14:10:30.349 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:10:30.488 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:10:30.562 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch234
2025-09-01 14:10:33.452 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 234/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 9.7, iou_loss: 2.7, l1_loss: 1.7, conf_loss: 4.4, cls_loss: 0.9, lr: 1.343e-03, size: 576, ETA: 2:05:00
2025-09-01 14:10:36.423 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 234/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.342e-03, size: 480, ETA: 2:04:56
2025-09-01 14:10:39.349 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 234/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, 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: 1.341e-03, size: 512, ETA: 2:04:52
2025-09-01 14:10:42.426 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 234/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 1.341e-03, size: 512, ETA: 2:04:49
2025-09-01 14:10:45.473 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 234/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 1.340e-03, size: 352, ETA: 2:04:46
2025-09-01 14:10:48.630 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 234/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.9, lr: 1.339e-03, size: 288, ETA: 2:04:42
2025-09-01 14:10:50.002 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:10:56.356 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:10:57.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:10:58.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5645
2025-09-01 14:10:58.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5018
2025-09-01 14:10:58.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3601
2025-09-01 14:10:58.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4755
2025-09-01 14:10:58.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:10:58.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:10:58.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.564
2025-09-01 14:10:58.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-09-01 14:10:58.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.360
2025-09-01 14:10:58.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.475
2025-09-01 14:10:58.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:10:58.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:10:58.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:10:58.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:10:58.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:10:58.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:10:58.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:10:58.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:10:58.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:10:59.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:10:59.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:11:00.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:11:01.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:11:02.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:11:03.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:11:03.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:11:04.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:11:05.586 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:11:05.587 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:11:05.587 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:11:05.587 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:11:05.595 | 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: 7.18 ms

2025-09-01 14:11:05.601 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:11:05.690 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:11:05.769 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch235
2025-09-01 14:11:08.606 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 235/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.9, lr: 1.338e-03, size: 320, ETA: 2:04:37
2025-09-01 14:11:11.527 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 235/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 1.337e-03, size: 352, ETA: 2:04:33
2025-09-01 14:11:14.420 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 235/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 1.336e-03, size: 320, ETA: 2:04:30
2025-09-01 14:11:17.310 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 235/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.6, lr: 1.336e-03, size: 448, ETA: 2:04:26
2025-09-01 14:11:20.346 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 235/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, 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: 1.335e-03, size: 352, ETA: 2:04:23
2025-09-01 14:11:23.381 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 235/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.9, lr: 1.334e-03, size: 320, ETA: 2:04:19
2025-09-01 14:11:24.810 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:11:31.029 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:11:31.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:11:31.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5640
2025-09-01 14:11:31.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4690
2025-09-01 14:11:32.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3578
2025-09-01 14:11:32.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4636
2025-09-01 14:11:32.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:11:32.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:11:32.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.564
2025-09-01 14:11:32.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.469
2025-09-01 14:11:32.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-09-01 14:11:32.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.464
2025-09-01 14:11:32.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:11:32.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:11:32.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:11:32.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:11:32.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:11:32.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:11:32.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:11:32.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:11:32.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:11:32.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:11:33.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:11:33.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:11:33.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:11:34.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:11:34.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:11:35.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:11:35.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:11:36.187 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:11:36.187 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:11:36.187 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 14:11:36.187 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:11:36.194 | 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

2025-09-01 14:11:36.199 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:11:36.375 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:11:36.448 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch236
2025-09-01 14:11:39.333 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 236/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.7, lr: 1.333e-03, size: 544, ETA: 2:04:14
2025-09-01 14:11:42.307 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 236/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.3, cls_loss: 0.7, lr: 1.332e-03, size: 544, ETA: 2:04:11
2025-09-01 14:11:45.463 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 236/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.4, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.5, lr: 1.332e-03, size: 480, ETA: 2:04:07
2025-09-01 14:11:48.538 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 236/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 1.331e-03, size: 384, ETA: 2:04:04
2025-09-01 14:11:51.577 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 236/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, 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: 0.6, lr: 1.330e-03, size: 480, ETA: 2:04:01
2025-09-01 14:11:54.624 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 236/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 8.2, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.9, lr: 1.329e-03, size: 256, ETA: 2:03:57
2025-09-01 14:11:55.949 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:12:02.223 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:12:03.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:12:03.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5920
2025-09-01 14:12:04.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5273
2025-09-01 14:12:04.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3584
2025-09-01 14:12:04.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4925
2025-09-01 14:12:04.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:12:04.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:12:04.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-09-01 14:12:04.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-09-01 14:12:04.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-09-01 14:12:04.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-09-01 14:12:04.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:12:04.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:12:04.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:12:04.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:12:04.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:12:04.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:12:04.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:12:04.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:12:04.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:12:05.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:12:06.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:12:07.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:12:07.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:12:08.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:12:09.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:12:10.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:12:11.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:12:12.422 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:12:12.422 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:12:12.422 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:12:12.422 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:12:12.430 | 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

2025-09-01 14:12:12.431 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:12:12.563 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:12:12.638 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch237
2025-09-01 14:12:15.541 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 237/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.9, lr: 1.328e-03, size: 256, ETA: 2:03:52
2025-09-01 14:12:18.590 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 237/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 1.327e-03, size: 384, ETA: 2:03:49
2025-09-01 14:12:21.590 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 237/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.9, lr: 1.327e-03, size: 352, ETA: 2:03:45
2025-09-01 14:12:24.619 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 237/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.6, lr: 1.326e-03, size: 384, ETA: 2:03:42
2025-09-01 14:12:27.722 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 237/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.4, cls_loss: 0.7, lr: 1.325e-03, size: 480, ETA: 2:03:38
2025-09-01 14:12:30.843 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 237/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.7, lr: 1.324e-03, size: 576, ETA: 2:03:35
2025-09-01 14:12:32.294 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:12:38.574 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:12:39.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:12:40.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5742
2025-09-01 14:12:41.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4927
2025-09-01 14:12:41.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3473
2025-09-01 14:12:41.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4714
2025-09-01 14:12:41.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:12:41.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:12:41.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-09-01 14:12:41.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-09-01 14:12:41.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-09-01 14:12:41.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.471
2025-09-01 14:12:41.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:12:41.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:12:41.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:12:41.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:12:41.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:12:41.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:12:41.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:12:41.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:12:41.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:12:42.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:12:43.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:12:44.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:12:45.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:12:46.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:12:47.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:12:49.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:12:50.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:12:51.448 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:12:51.448 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:12:51.448 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 14:12:51.448 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:12:51.456 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.36 ms, Average NMS time: 0.94 ms, Average inference time: 7.29 ms

2025-09-01 14:12:51.462 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:12:51.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:12:51.644 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch238
2025-09-01 14:12:54.545 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 238/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.6, lr: 1.323e-03, size: 480, ETA: 2:03:30
2025-09-01 14:12:57.498 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 238/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, 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: 1.322e-03, size: 320, ETA: 2:03:26
2025-09-01 14:13:00.501 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 238/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.8, lr: 1.322e-03, size: 352, ETA: 2:03:23
2025-09-01 14:13:03.511 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 238/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.321e-03, size: 256, ETA: 2:03:20
2025-09-01 14:13:06.615 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 238/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 9.8, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 4.8, cls_loss: 0.8, lr: 1.320e-03, size: 288, ETA: 2:03:16
2025-09-01 14:13:09.567 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 238/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.003s, total_loss: 9.5, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 4.3, cls_loss: 0.8, lr: 1.319e-03, size: 288, ETA: 2:03:13
2025-09-01 14:13:10.941 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:13:17.419 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:13:18.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:13:18.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5762
2025-09-01 14:13:18.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4937
2025-09-01 14:13:19.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3717
2025-09-01 14:13:19.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4805
2025-09-01 14:13:19.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:13:19.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:13:19.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-09-01 14:13:19.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-09-01 14:13:19.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-09-01 14:13:19.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-09-01 14:13:19.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:13:19.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:13:19.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:13:19.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:13:19.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:13:19.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:13:19.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:13:19.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:13:19.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:13:19.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:13:20.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:13:21.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:13:21.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:13:22.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:13:23.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:13:24.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:13:24.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:13:25.414 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:13:25.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:13:25.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:13:25.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:13:25.424 | 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

2025-09-01 14:13:25.425 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:13:25.516 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:13:25.599 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch239
2025-09-01 14:13:28.567 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 239/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.8, lr: 1.318e-03, size: 256, ETA: 2:03:08
2025-09-01 14:13:31.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 239/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 2.3, cls_loss: 0.6, lr: 1.317e-03, size: 416, ETA: 2:03:04
2025-09-01 14:13:34.448 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 239/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.317e-03, size: 416, ETA: 2:03:00
2025-09-01 14:13:37.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 239/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.316e-03, size: 512, ETA: 2:02:57
2025-09-01 14:13:40.513 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 239/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, 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: 1.315e-03, size: 480, ETA: 2:02:54
2025-09-01 14:13:43.534 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 239/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 9.7, iou_loss: 2.9, l1_loss: 1.6, conf_loss: 4.4, cls_loss: 0.7, lr: 1.314e-03, size: 512, ETA: 2:02:50
2025-09-01 14:13:44.909 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:13:51.408 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:13:52.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:13:53.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5816
2025-09-01 14:13:53.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4809
2025-09-01 14:13:53.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3902
2025-09-01 14:13:53.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4842
2025-09-01 14:13:53.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:13:53.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:13:53.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-09-01 14:13:53.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.481
2025-09-01 14:13:53.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-09-01 14:13:53.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-09-01 14:13:53.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:13:53.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:13:53.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:13:53.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:13:53.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:13:53.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:13:53.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:13:53.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:13:53.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:13:54.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:13:54.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:13:55.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:13:56.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:13:57.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:13:58.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:13:58.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:13:59.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:14:00.492 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:14:00.492 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:14:00.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:14:00.493 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:14:00.500 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.36 ms, Average NMS time: 0.91 ms, Average inference time: 7.27 ms

2025-09-01 14:14:00.501 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:14:00.590 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:14:00.671 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch240
2025-09-01 14:14:03.478 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 240/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.138s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 1.313e-03, size: 576, ETA: 2:02:45
2025-09-01 14:14:06.579 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 240/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, 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: 1.312e-03, size: 384, ETA: 2:02:42
2025-09-01 14:14:09.541 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 240/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.7, lr: 1.312e-03, size: 320, ETA: 2:02:38
2025-09-01 14:14:12.499 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 240/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.311e-03, size: 448, ETA: 2:02:35
2025-09-01 14:14:15.643 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 240/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.310e-03, size: 576, ETA: 2:02:31
2025-09-01 14:14:18.866 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 240/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.159s, data_time: 0.006s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.7, lr: 1.309e-03, size: 320, ETA: 2:02:28
2025-09-01 14:14:20.201 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:14:26.511 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:14:27.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:14:28.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5881
2025-09-01 14:14:28.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5062
2025-09-01 14:14:28.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4061
2025-09-01 14:14:28.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5001
2025-09-01 14:14:28.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:14:28.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:14:28.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-09-01 14:14:28.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-09-01 14:14:28.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-09-01 14:14:28.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-09-01 14:14:28.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:14:28.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:14:28.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:14:28.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:14:28.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:14:28.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:14:28.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:14:28.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:14:28.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:14:29.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:14:30.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:14:30.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:14:31.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:14:32.504 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:14:33.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:14:34.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:14:35.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:14:35.895 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:14:35.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:14:35.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 14:14:35.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:14:35.903 | 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

2025-09-01 14:14:35.904 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:14:36.032 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:14:36.115 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch241
2025-09-01 14:14:38.952 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 241/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.140s, 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: 1.308e-03, size: 512, ETA: 2:02:23
2025-09-01 14:14:42.100 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 241/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.7, lr: 1.307e-03, size: 544, ETA: 2:02:20
2025-09-01 14:14:45.071 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 241/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.2, cls_loss: 0.7, lr: 1.307e-03, size: 512, ETA: 2:02:16
2025-09-01 14:14:48.138 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 241/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 1.306e-03, size: 544, ETA: 2:02:13
2025-09-01 14:14:51.079 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 241/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 1.305e-03, size: 320, ETA: 2:02:09
2025-09-01 14:14:54.032 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 241/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.003s, total_loss: 8.1, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.304e-03, size: 256, ETA: 2:02:06
2025-09-01 14:14:55.371 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:15:01.664 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:15:02.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:15:03.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5844
2025-09-01 14:15:03.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5151
2025-09-01 14:15:03.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3499
2025-09-01 14:15:03.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4831
2025-09-01 14:15:03.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:15:03.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:15:03.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-09-01 14:15:03.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 14:15:03.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.350
2025-09-01 14:15:03.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.483
2025-09-01 14:15:03.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:15:03.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:15:03.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:15:03.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:15:03.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:15:03.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:15:03.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:15:03.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:15:03.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:15:04.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:15:05.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:15:06.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:15:07.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:15:08.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:15:09.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:15:10.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:15:11.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:15:12.382 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:15:12.382 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 14:15:12.382 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:15:12.382 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:15:12.390 | 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: 7.20 ms

2025-09-01 14:15:12.391 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:15:12.473 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:15:12.557 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch242
2025-09-01 14:15:15.344 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 242/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.138s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.7, lr: 1.303e-03, size: 512, ETA: 2:02:00
2025-09-01 14:15:18.363 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 242/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.8, lr: 1.302e-03, size: 288, ETA: 2:01:57
2025-09-01 14:15:21.527 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 242/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 1.302e-03, size: 320, ETA: 2:01:54
2025-09-01 14:15:24.559 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 242/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.301e-03, size: 544, ETA: 2:01:50
2025-09-01 14:15:27.621 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 242/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.300e-03, size: 352, ETA: 2:01:47
2025-09-01 14:15:30.749 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 242/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 1.299e-03, size: 352, ETA: 2:01:44
2025-09-01 14:15:32.121 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:15:38.239 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:15:39.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:15:40.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5843
2025-09-01 14:15:40.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5057
2025-09-01 14:15:40.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3702
2025-09-01 14:15:40.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4868
2025-09-01 14:15:40.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:15:40.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:15:40.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-09-01 14:15:40.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-09-01 14:15:40.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-09-01 14:15:40.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-09-01 14:15:40.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:15:40.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:15:40.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:15:40.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:15:40.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:15:40.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:15:40.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:15:40.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:15:40.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:15:41.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:15:42.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:15:43.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:15:44.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:15:45.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:15:46.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:15:47.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:15:48.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:15:49.697 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:15:49.697 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 14:15:49.697 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:15:49.697 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:15:49.705 | 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

2025-09-01 14:15:49.707 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:15:49.827 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:15:49.905 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch243
2025-09-01 14:15:52.777 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 243/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.298e-03, size: 448, ETA: 2:01:38
2025-09-01 14:15:55.871 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 243/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 4.0, cls_loss: 1.5, lr: 1.297e-03, size: 288, ETA: 2:01:35
2025-09-01 14:15:58.811 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 243/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 1.297e-03, size: 512, ETA: 2:01:31
2025-09-01 14:16:01.957 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 243/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.5, cls_loss: 1.1, lr: 1.296e-03, size: 576, ETA: 2:01:28
2025-09-01 14:16:05.175 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 243/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.5, conf_loss: 1.6, cls_loss: 0.6, lr: 1.295e-03, size: 576, ETA: 2:01:25
2025-09-01 14:16:08.394 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 243/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 3.5, cls_loss: 0.8, lr: 1.294e-03, size: 448, ETA: 2:01:22
2025-09-01 14:16:09.768 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:16:15.890 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:16:17.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:16:18.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5784
2025-09-01 14:16:18.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4986
2025-09-01 14:16:18.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3710
2025-09-01 14:16:18.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4827
2025-09-01 14:16:18.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:16:18.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:16:18.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-09-01 14:16:18.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-09-01 14:16:18.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-09-01 14:16:18.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.483
2025-09-01 14:16:18.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:16:18.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:16:18.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:16:18.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:16:18.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:16:18.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:16:18.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:16:18.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:16:18.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:16:19.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:16:21.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:16:22.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:16:23.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:16:24.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:16:26.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:16:27.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:16:28.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:16:29.754 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:16:29.755 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:16:29.755 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:16:29.755 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:16:29.763 | 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

2025-09-01 14:16:29.764 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:16:29.844 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:16:29.928 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch244
2025-09-01 14:16:32.785 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 244/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 1.293e-03, size: 288, ETA: 2:01:17
2025-09-01 14:16:35.859 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 244/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.292e-03, size: 416, ETA: 2:01:13
2025-09-01 14:16:38.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 244/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.2, cls_loss: 0.7, lr: 1.292e-03, size: 512, ETA: 2:01:10
2025-09-01 14:16:41.985 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 244/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.6, lr: 1.291e-03, size: 448, ETA: 2:01:07
2025-09-01 14:16:44.997 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 244/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 1.290e-03, size: 320, ETA: 2:01:03
2025-09-01 14:16:48.072 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 244/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 1.289e-03, size: 480, ETA: 2:01:00
2025-09-01 14:16:49.421 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:16:55.686 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:16:56.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:16:57.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5836
2025-09-01 14:16:57.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5281
2025-09-01 14:16:57.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3787
2025-09-01 14:16:57.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4968
2025-09-01 14:16:57.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:16:57.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:16:57.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-09-01 14:16:57.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-09-01 14:16:57.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-09-01 14:16:57.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-09-01 14:16:57.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:16:57.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:16:57.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:16:57.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:16:57.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:16:57.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:16:57.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:16:57.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:16:57.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:16:58.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:16:58.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:16:59.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:17:00.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:17:01.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:17:01.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:17:02.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:17:03.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:17:04.008 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:17:04.008 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:17:04.008 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 14:17:04.008 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:17:04.015 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.94 ms, Average inference time: 7.25 ms

2025-09-01 14:17:04.017 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:17:04.104 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:17:04.186 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch245
2025-09-01 14:17:07.097 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 245/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.4, conf_loss: 2.6, cls_loss: 0.7, lr: 1.288e-03, size: 448, ETA: 2:00:55
2025-09-01 14:17:10.067 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 245/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 1.287e-03, size: 512, ETA: 2:00:51
2025-09-01 14:17:13.122 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 245/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.5, lr: 1.287e-03, size: 384, ETA: 2:00:48
2025-09-01 14:17:16.284 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 245/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, 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: 1.286e-03, size: 288, ETA: 2:00:45
2025-09-01 14:17:19.322 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 245/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.285e-03, size: 288, ETA: 2:00:41
2025-09-01 14:17:22.370 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 245/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 1.284e-03, size: 384, ETA: 2:00:38
2025-09-01 14:17:23.676 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:17:29.944 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:17:30.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:17:31.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5799
2025-09-01 14:17:31.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4835
2025-09-01 14:17:31.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3633
2025-09-01 14:17:31.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4756
2025-09-01 14:17:31.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:17:31.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:17:31.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.580
2025-09-01 14:17:31.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.484
2025-09-01 14:17:31.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-09-01 14:17:31.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.476
2025-09-01 14:17:31.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:17:31.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:17:31.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:17:31.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:17:31.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:17:31.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:17:31.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:17:31.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:17:31.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:17:32.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:17:32.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:17:33.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:17:33.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:17:34.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:17:35.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:17:35.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:17:36.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:17:37.150 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:17:37.150 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:17:37.151 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:17:37.151 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:17:37.164 | 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

2025-09-01 14:17:37.165 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:17:37.315 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:17:37.411 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch246
2025-09-01 14:17:40.283 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 246/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 0.9, lr: 1.283e-03, size: 576, ETA: 2:00:32
2025-09-01 14:17:43.418 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 246/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 2.6, cls_loss: 0.7, lr: 1.282e-03, size: 576, ETA: 2:00:29
2025-09-01 14:17:46.563 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 246/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.6, lr: 1.282e-03, size: 384, ETA: 2:00:26
2025-09-01 14:17:49.602 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 246/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, 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: 1.281e-03, size: 384, ETA: 2:00:23
2025-09-01 14:17:52.630 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 246/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 3.4, cls_loss: 0.6, lr: 1.280e-03, size: 416, ETA: 2:00:19
2025-09-01 14:17:55.604 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 246/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.7, lr: 1.279e-03, size: 256, ETA: 2:00:16
2025-09-01 14:17:56.930 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:18:03.120 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:18:04.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:18:04.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5309
2025-09-01 14:18:05.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5018
2025-09-01 14:18:05.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3666
2025-09-01 14:18:05.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4664
2025-09-01 14:18:05.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:18:05.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:18:05.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-09-01 14:18:05.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-09-01 14:18:05.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.367
2025-09-01 14:18:05.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.466
2025-09-01 14:18:05.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:18:05.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:18:05.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:18:05.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:18:05.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:18:05.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:18:05.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:18:05.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:18:05.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:18:05.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:18:06.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:18:07.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:18:08.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:18:09.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:18:10.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:18:11.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:18:12.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:18:12.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:18:12.942 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 14:18:12.942 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 14:18:12.942 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:18:12.949 | 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

2025-09-01 14:18:12.954 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:18:13.040 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:18:13.134 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch247
2025-09-01 14:18:15.950 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 247/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.8, lr: 1.278e-03, size: 256, ETA: 2:00:10
2025-09-01 14:18:18.962 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 247/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 1.277e-03, size: 480, ETA: 2:00:07
2025-09-01 14:18:22.043 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 247/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 4.2, cls_loss: 0.6, lr: 1.277e-03, size: 320, ETA: 2:00:04
2025-09-01 14:18:25.054 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 247/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 1.276e-03, size: 576, ETA: 2:00:00
2025-09-01 14:18:28.166 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 247/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 1.275e-03, size: 320, ETA: 1:59:57
2025-09-01 14:18:31.287 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 247/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 1.0, lr: 1.274e-03, size: 256, ETA: 1:59:54
2025-09-01 14:18:32.551 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:18:38.735 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:18:40.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:18:42.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5937
2025-09-01 14:18:42.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5054
2025-09-01 14:18:42.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3612
2025-09-01 14:18:42.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4868
2025-09-01 14:18:42.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:18:42.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:18:42.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.594
2025-09-01 14:18:42.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-09-01 14:18:42.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-09-01 14:18:42.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-09-01 14:18:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:18:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:18:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:18:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:18:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:18:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:18:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:18:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:18:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:18:44.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:18:46.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:18:47.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:18:49.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:18:51.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:18:53.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:18:55.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:18:56.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:18:58.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:18:58.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:18:58.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:18:58.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:18:58.632 | 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: 7.27 ms

2025-09-01 14:18:58.633 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:18:58.783 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:18:58.866 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch248
2025-09-01 14:19:01.864 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 248/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 1.273e-03, size: 480, ETA: 1:59:49
2025-09-01 14:19:05.013 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 248/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 1.272e-03, size: 448, ETA: 1:59:45
2025-09-01 14:19:08.195 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 248/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 1.272e-03, size: 448, ETA: 1:59:42
2025-09-01 14:19:11.316 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 248/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, 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: 1.271e-03, size: 448, ETA: 1:59:39
2025-09-01 14:19:14.479 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 248/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.8, lr: 1.270e-03, size: 256, ETA: 1:59:36
2025-09-01 14:19:17.700 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 248/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.159s, data_time: 0.003s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.5, lr: 1.269e-03, size: 384, ETA: 1:59:33
2025-09-01 14:19:19.047 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:19:25.213 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:19:26.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:19:26.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5921
2025-09-01 14:19:26.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5163
2025-09-01 14:19:26.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3441
2025-09-01 14:19:26.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4842
2025-09-01 14:19:26.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:19:26.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:19:26.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-09-01 14:19:26.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-09-01 14:19:26.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.344
2025-09-01 14:19:26.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-09-01 14:19:26.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:19:26.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:19:26.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:19:26.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:19:26.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:19:26.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:19:26.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:19:26.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:19:26.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:19:27.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:19:28.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:19:29.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:19:29.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:19:30.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:19:31.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:19:32.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:19:32.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:19:33.581 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:19:33.581 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:19:33.582 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:19:33.582 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:19:33.595 | 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

2025-09-01 14:19:33.596 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:19:33.715 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:19:33.818 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch249
2025-09-01 14:19:36.776 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 249/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.6, lr: 1.268e-03, size: 480, ETA: 1:59:27
2025-09-01 14:19:39.781 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 249/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.9, lr: 1.267e-03, size: 288, ETA: 1:59:24
2025-09-01 14:19:42.758 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 249/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, 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: 1.267e-03, size: 256, ETA: 1:59:21
2025-09-01 14:19:45.777 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 249/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 1.266e-03, size: 512, ETA: 1:59:17
2025-09-01 14:19:48.794 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 249/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.265e-03, size: 448, ETA: 1:59:14
2025-09-01 14:19:51.779 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 249/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 1.264e-03, size: 416, ETA: 1:59:10
2025-09-01 14:19:53.213 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:19:59.460 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:20:00.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:20:01.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5853
2025-09-01 14:20:01.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4957
2025-09-01 14:20:01.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3687
2025-09-01 14:20:01.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4832
2025-09-01 14:20:01.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:20:01.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:20:01.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.585
2025-09-01 14:20:01.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-09-01 14:20:01.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-09-01 14:20:01.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.483
2025-09-01 14:20:01.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:20:01.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:20:01.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:20:01.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:20:01.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:20:01.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:20:01.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:20:01.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:20:01.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:20:02.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:20:02.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:20:03.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:20:04.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:20:05.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:20:06.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:20:07.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:20:07.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:20:08.765 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:20:08.765 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:20:08.765 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:20:08.765 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:20:08.773 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.41 ms, Average NMS time: 0.95 ms, Average inference time: 7.36 ms

2025-09-01 14:20:08.776 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:20:08.862 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:20:08.947 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch250
2025-09-01 14:20:11.782 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 250/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.263e-03, size: 352, ETA: 1:59:05
2025-09-01 14:20:14.929 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 250/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.262e-03, size: 352, ETA: 1:59:02
2025-09-01 14:20:17.877 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 250/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.262e-03, size: 384, ETA: 1:58:58
2025-09-01 14:20:20.901 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 250/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 1.261e-03, size: 384, ETA: 1:58:55
2025-09-01 14:20:23.918 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 250/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 1.260e-03, size: 512, ETA: 1:58:52
2025-09-01 14:20:26.953 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 250/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.0, l1_loss: 0.2, conf_loss: 2.2, cls_loss: 0.4, lr: 1.259e-03, size: 288, ETA: 1:58:48
2025-09-01 14:20:28.241 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:20:34.450 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:20:35.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:20:36.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5815
2025-09-01 14:20:36.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5087
2025-09-01 14:20:36.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3590
2025-09-01 14:20:36.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4831
2025-09-01 14:20:36.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:20:36.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:20:36.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.581
2025-09-01 14:20:36.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-09-01 14:20:36.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-09-01 14:20:36.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.483
2025-09-01 14:20:36.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:20:36.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:20:36.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:20:36.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:20:36.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:20:36.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:20:36.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:20:36.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:20:36.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:20:37.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:20:38.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:20:39.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:20:40.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:20:41.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:20:42.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:20:43.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:20:44.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:20:45.006 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:20:45.007 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:20:45.007 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:20:45.007 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:20:45.015 | 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: 7.07 ms

2025-09-01 14:20:45.015 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:20:45.148 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:20:45.224 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch251
2025-09-01 14:20:48.074 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 251/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 3.7, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 1.258e-03, size: 352, ETA: 1:58:43
2025-09-01 14:20:51.160 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 251/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.8, lr: 1.257e-03, size: 512, ETA: 1:58:40
2025-09-01 14:20:54.211 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 251/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 1.256e-03, size: 448, ETA: 1:58:36
2025-09-01 14:20:57.132 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 251/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.8, lr: 1.256e-03, size: 384, ETA: 1:58:33
2025-09-01 14:21:00.033 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 251/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 3.7, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.5, lr: 1.255e-03, size: 416, ETA: 1:58:29
2025-09-01 14:21:02.983 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 251/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, 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: 1.254e-03, size: 384, ETA: 1:58:26
2025-09-01 14:21:04.319 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:21:10.577 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:21:11.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:21:11.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5717
2025-09-01 14:21:11.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4673
2025-09-01 14:21:11.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3423
2025-09-01 14:21:11.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4604
2025-09-01 14:21:11.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:21:11.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:21:11.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-09-01 14:21:11.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.467
2025-09-01 14:21:11.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.342
2025-09-01 14:21:11.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.460
2025-09-01 14:21:11.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:21:11.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:21:11.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:21:11.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:21:11.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:21:11.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:21:11.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:21:11.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:21:11.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:21:12.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:21:12.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:21:13.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:21:13.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:21:14.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:21:15.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:21:15.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:21:16.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:21:16.787 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:21:16.788 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 14:21:16.788 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 14:21:16.788 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:21:16.795 | 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: 7.09 ms

2025-09-01 14:21:16.796 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:21:16.887 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:21:17.006 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch252
2025-09-01 14:21:19.990 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 252/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 1.253e-03, size: 544, ETA: 1:58:21
2025-09-01 14:21:23.200 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 252/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 0.8, cls_loss: 0.5, lr: 1.252e-03, size: 512, ETA: 1:58:17
2025-09-01 14:21:26.169 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 252/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.7, lr: 1.251e-03, size: 288, ETA: 1:58:14
2025-09-01 14:21:29.312 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 252/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 1.251e-03, size: 576, ETA: 1:58:11
2025-09-01 14:21:32.296 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 252/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.5, lr: 1.250e-03, size: 288, ETA: 1:58:07
2025-09-01 14:21:35.403 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 252/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, 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: 0.7, lr: 1.249e-03, size: 416, ETA: 1:58:04
2025-09-01 14:21:36.807 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:21:43.186 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:21:44.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:21:45.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5751
2025-09-01 14:21:45.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5165
2025-09-01 14:21:45.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3984
2025-09-01 14:21:45.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4967
2025-09-01 14:21:45.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:21:45.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:21:45.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.575
2025-09-01 14:21:45.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 14:21:45.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.398
2025-09-01 14:21:45.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-09-01 14:21:45.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:21:45.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:21:45.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:21:45.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:21:45.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:21:45.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:21:45.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:21:45.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:21:45.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:21:46.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:21:47.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:21:48.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:21:49.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:21:50.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:21:51.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:21:52.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:21:53.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:21:54.295 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:21:54.296 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 14:21:54.296 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 14:21:54.297 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:21:54.310 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.93 ms, Average inference time: 7.19 ms

2025-09-01 14:21:54.311 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:21:54.428 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:21:54.537 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch253
2025-09-01 14:21:57.541 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 253/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.9, lr: 1.248e-03, size: 448, ETA: 1:57:59
2025-09-01 14:22:00.665 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 253/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 1.247e-03, size: 512, ETA: 1:57:56
2025-09-01 14:22:03.711 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 253/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 2.0, cls_loss: 0.7, lr: 1.246e-03, size: 576, ETA: 1:57:53
2025-09-01 14:22:06.720 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 253/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.246e-03, size: 448, ETA: 1:57:49
2025-09-01 14:22:09.669 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 253/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.4, iou_loss: 1.9, l1_loss: 1.3, conf_loss: 3.6, cls_loss: 0.6, lr: 1.245e-03, size: 544, ETA: 1:57:46
2025-09-01 14:22:12.701 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 253/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.7, lr: 1.244e-03, size: 288, ETA: 1:57:42
2025-09-01 14:22:14.004 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:22:20.057 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:22:20.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:22:21.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5724
2025-09-01 14:22:21.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5050
2025-09-01 14:22:21.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3418
2025-09-01 14:22:21.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4730
2025-09-01 14:22:21.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:22:21.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:22:21.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-09-01 14:22:21.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-09-01 14:22:21.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.342
2025-09-01 14:22:21.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.473
2025-09-01 14:22:21.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:22:21.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:22:21.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:22:21.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:22:21.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:22:21.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:22:21.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:22:21.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:22:21.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:22:22.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:22:22.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:22:23.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:22:24.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:22:25.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:22:25.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:22:26.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:22:27.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:22:27.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:22:27.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:22:27.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 14:22:27.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:22:27.903 | 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

2025-09-01 14:22:27.904 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:22:27.992 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:22:28.073 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch254
2025-09-01 14:22:30.909 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 254/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.6, lr: 1.243e-03, size: 352, ETA: 1:57:37
2025-09-01 14:22:33.936 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 254/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 1.242e-03, size: 320, ETA: 1:57:34
2025-09-01 14:22:36.968 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 254/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 2.3, cls_loss: 0.7, lr: 1.241e-03, size: 576, ETA: 1:57:30
2025-09-01 14:22:40.103 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 254/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 10.1, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 4.8, cls_loss: 1.0, lr: 1.240e-03, size: 576, ETA: 1:57:27
2025-09-01 14:22:43.204 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 254/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 17.8, iou_loss: 4.1, l1_loss: 1.2, conf_loss: 11.7, cls_loss: 0.8, lr: 1.240e-03, size: 512, ETA: 1:57:24
2025-09-01 14:22:46.298 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 254/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 1.239e-03, size: 384, ETA: 1:57:20
2025-09-01 14:22:47.659 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:22:53.808 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:22:54.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:22:55.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5841
2025-09-01 14:22:55.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5159
2025-09-01 14:22:55.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3793
2025-09-01 14:22:55.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4931
2025-09-01 14:22:55.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:22:55.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:22:55.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-09-01 14:22:55.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-09-01 14:22:55.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-09-01 14:22:55.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-09-01 14:22:55.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:22:55.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:22:55.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:22:55.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:22:55.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:22:55.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:22:55.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:22:55.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:22:55.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:22:55.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:22:56.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:22:57.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:22:57.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:22:58.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:22:58.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:22:59.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:23:00.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:23:00.833 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:23:00.833 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:23:00.833 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:23:00.834 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:23:00.841 | 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

2025-09-01 14:23:00.842 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:23:00.974 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:23:01.049 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch255
2025-09-01 14:23:03.948 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 255/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.238e-03, size: 480, ETA: 1:57:15
2025-09-01 14:23:07.064 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 255/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 1.237e-03, size: 256, ETA: 1:57:12
2025-09-01 14:23:10.078 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 255/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 1.236e-03, size: 320, ETA: 1:57:09
2025-09-01 14:23:13.096 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 255/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 1.235e-03, size: 384, ETA: 1:57:05
2025-09-01 14:23:16.086 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 255/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.6, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 2.6, cls_loss: 0.6, lr: 1.235e-03, size: 416, ETA: 1:57:02
2025-09-01 14:23:19.042 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 255/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 1.4, cls_loss: 0.5, lr: 1.234e-03, size: 256, ETA: 1:56:58
2025-09-01 14:23:20.453 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:23:26.579 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:23:27.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:23:28.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5834
2025-09-01 14:23:28.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5015
2025-09-01 14:23:28.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3894
2025-09-01 14:23:28.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4914
2025-09-01 14:23:28.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:23:28.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:23:28.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.583
2025-09-01 14:23:28.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-09-01 14:23:28.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-09-01 14:23:28.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.491
2025-09-01 14:23:28.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:23:28.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:23:28.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:23:28.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:23:28.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:23:28.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:23:28.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:23:28.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:23:28.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:23:28.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:23:29.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:23:30.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:23:31.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:23:31.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:23:32.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:23:33.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:23:34.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:23:34.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:23:34.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:23:34.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:23:34.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:23:34.858 | 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: 7.17 ms

2025-09-01 14:23:34.863 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:23:34.956 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:23:35.038 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch256
2025-09-01 14:23:37.978 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 256/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.6, iou_loss: 1.8, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.6, lr: 1.233e-03, size: 576, ETA: 1:56:53
2025-09-01 14:23:41.098 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 256/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, 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: 1.232e-03, size: 512, ETA: 1:56:50
2025-09-01 14:23:44.253 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 256/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.156s, 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: 1.231e-03, size: 320, ETA: 1:56:47
2025-09-01 14:23:47.192 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 256/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 3.7, cls_loss: 0.8, lr: 1.230e-03, size: 480, ETA: 1:56:43
2025-09-01 14:23:50.296 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 256/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 1.0, lr: 1.229e-03, size: 384, ETA: 1:56:40
2025-09-01 14:23:53.342 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 256/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 1.229e-03, size: 416, ETA: 1:56:37
2025-09-01 14:23:54.675 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:24:00.830 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:24:01.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:24:02.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6071
2025-09-01 14:24:02.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5354
2025-09-01 14:24:02.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3693
2025-09-01 14:24:02.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5040
2025-09-01 14:24:02.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:24:02.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:24:02.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-09-01 14:24:02.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-09-01 14:24:02.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-09-01 14:24:02.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-09-01 14:24:02.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:24:02.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:24:02.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:24:02.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:24:02.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:24:02.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:24:02.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:24:02.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:24:02.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:24:03.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:24:04.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:24:05.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:24:06.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:24:07.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:24:08.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:24:09.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:24:10.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:24:11.261 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:24:11.261 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 14:24:11.262 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 14:24:11.262 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:24:11.269 | 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: 7.16 ms

2025-09-01 14:24:11.271 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:24:11.353 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:24:11.437 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch257
2025-09-01 14:24:14.256 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 257/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.8, lr: 1.228e-03, size: 384, ETA: 1:56:32
2025-09-01 14:24:17.242 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 257/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.2, cls_loss: 0.7, lr: 1.227e-03, size: 480, ETA: 1:56:28
2025-09-01 14:24:20.315 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 257/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.7, lr: 1.226e-03, size: 544, ETA: 1:56:25
2025-09-01 14:24:23.338 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 257/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.7, lr: 1.225e-03, size: 352, ETA: 1:56:21
2025-09-01 14:24:26.343 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 257/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.224e-03, size: 512, ETA: 1:56:18
2025-09-01 14:24:29.382 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 257/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 1.3, lr: 1.224e-03, size: 352, ETA: 1:56:15
2025-09-01 14:24:30.682 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:24:36.821 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:24:37.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:24:38.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5573
2025-09-01 14:24:38.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4709
2025-09-01 14:24:38.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3006
2025-09-01 14:24:38.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4429
2025-09-01 14:24:38.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:24:38.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:24:38.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.557
2025-09-01 14:24:38.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.471
2025-09-01 14:24:38.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.301
2025-09-01 14:24:38.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.443
2025-09-01 14:24:38.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:24:38.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:24:38.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:24:38.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:24:38.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:24:38.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:24:38.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:24:38.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:24:38.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:24:38.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:24:39.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:24:40.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:24:40.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:24:41.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:24:42.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:24:42.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:24:43.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:24:43.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:24:43.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 14:24:43.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.44
2025-09-01 14:24:43.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:24:43.948 | 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

2025-09-01 14:24:43.950 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:24:44.034 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:24:44.118 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch258
2025-09-01 14:24:47.033 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 258/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 3.4, cls_loss: 0.9, lr: 1.222e-03, size: 448, ETA: 1:56:10
2025-09-01 14:24:50.014 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 258/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.7, lr: 1.222e-03, size: 320, ETA: 1:56:06
2025-09-01 14:24:52.973 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 258/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.8, cls_loss: 0.7, lr: 1.221e-03, size: 480, ETA: 1:56:03
2025-09-01 14:24:55.938 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 258/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.8, cls_loss: 0.7, lr: 1.220e-03, size: 384, ETA: 1:55:59
2025-09-01 14:24:58.893 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 258/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, 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: 1.219e-03, size: 320, ETA: 1:55:56
2025-09-01 14:25:01.864 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 258/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 1.218e-03, size: 416, ETA: 1:55:52
2025-09-01 14:25:03.239 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:25:09.501 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:25:10.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:25:10.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5997
2025-09-01 14:25:11.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5246
2025-09-01 14:25:11.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3724
2025-09-01 14:25:11.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4989
2025-09-01 14:25:11.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:25:11.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:25:11.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-09-01 14:25:11.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-09-01 14:25:11.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-09-01 14:25:11.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.499
2025-09-01 14:25:11.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:25:11.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:25:11.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:25:11.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:25:11.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:25:11.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:25:11.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:25:11.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:25:11.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:25:12.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:25:12.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:25:13.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:25:14.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:25:15.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:25:15.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:25:16.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:25:17.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:25:18.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:25:18.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:25:18.234 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 14:25:18.235 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:25:18.242 | 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: 7.16 ms

2025-09-01 14:25:18.243 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:25:18.368 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:25:18.443 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch259
2025-09-01 14:25:21.298 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 259/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.6, lr: 1.217e-03, size: 416, ETA: 1:55:47
2025-09-01 14:25:24.325 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 259/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.217e-03, size: 512, ETA: 1:55:44
2025-09-01 14:25:27.342 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 259/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 1.216e-03, size: 512, ETA: 1:55:40
2025-09-01 14:25:30.304 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 259/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.9, lr: 1.215e-03, size: 416, ETA: 1:55:37
2025-09-01 14:25:33.260 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 259/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.214e-03, size: 384, ETA: 1:55:34
2025-09-01 14:25:36.300 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 259/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.7, lr: 1.213e-03, size: 384, ETA: 1:55:30
2025-09-01 14:25:37.646 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:25:43.946 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:25:45.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:25:45.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5968
2025-09-01 14:25:45.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5222
2025-09-01 14:25:45.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3741
2025-09-01 14:25:45.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4977
2025-09-01 14:25:45.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:25:45.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:25:45.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.597
2025-09-01 14:25:45.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-09-01 14:25:45.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.374
2025-09-01 14:25:45.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.498
2025-09-01 14:25:45.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:25:45.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:25:45.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:25:45.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:25:45.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:25:45.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:25:45.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:25:45.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:25:45.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:25:46.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:25:47.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:25:48.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:25:49.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:25:50.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:25:51.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:25:52.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:25:53.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:25:54.426 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:25:54.426 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:25:54.427 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 14:25:54.427 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:25:54.434 | 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

2025-09-01 14:25:54.435 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:25:54.522 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:25:54.604 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch260
2025-09-01 14:25:57.476 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 260/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 1.5, conf_loss: 2.3, cls_loss: 0.8, lr: 1.212e-03, size: 480, ETA: 1:55:25
2025-09-01 14:26:00.615 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 260/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 1.211e-03, size: 320, ETA: 1:55:22
2025-09-01 14:26:03.682 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 260/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 0.8, lr: 1.211e-03, size: 256, ETA: 1:55:19
2025-09-01 14:26:06.549 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 260/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 1.210e-03, size: 448, ETA: 1:55:15
2025-09-01 14:26:09.567 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 260/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 1.209e-03, size: 320, ETA: 1:55:12
2025-09-01 14:26:12.655 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 260/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.8, lr: 1.208e-03, size: 352, ETA: 1:55:08
2025-09-01 14:26:13.976 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:26:20.262 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:26:21.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:26:21.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5966
2025-09-01 14:26:21.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5215
2025-09-01 14:26:21.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3663
2025-09-01 14:26:21.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4948
2025-09-01 14:26:21.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:26:21.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:26:21.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.597
2025-09-01 14:26:21.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 14:26:21.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-09-01 14:26:21.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-09-01 14:26:21.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:26:21.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:26:21.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:26:21.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:26:21.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:26:21.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:26:21.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:26:21.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:26:21.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:26:22.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:26:23.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:26:24.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:26:24.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:26:25.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:26:26.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:26:27.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:26:27.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:26:28.594 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:26:28.594 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:26:28.594 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:26:28.594 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:26:28.602 | 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

2025-09-01 14:26:28.603 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:26:28.690 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:26:28.774 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch261
2025-09-01 14:26:31.629 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 261/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.6, lr: 1.207e-03, size: 416, ETA: 1:55:03
2025-09-01 14:26:34.675 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 261/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 1.9, cls_loss: 0.8, lr: 1.206e-03, size: 384, ETA: 1:55:00
2025-09-01 14:26:37.728 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 261/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.6, lr: 1.206e-03, size: 576, ETA: 1:54:56
2025-09-01 14:26:40.699 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 261/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.6, lr: 1.205e-03, size: 384, ETA: 1:54:53
2025-09-01 14:26:43.850 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 261/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.6, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.5, lr: 1.204e-03, size: 480, ETA: 1:54:50
2025-09-01 14:26:46.929 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 261/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 2.5, cls_loss: 0.7, lr: 1.203e-03, size: 544, ETA: 1:54:47
2025-09-01 14:26:48.257 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:26:54.571 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:26:55.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:26:56.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5933
2025-09-01 14:26:56.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5146
2025-09-01 14:26:56.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3635
2025-09-01 14:26:56.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4905
2025-09-01 14:26:56.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:26:56.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:26:56.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-09-01 14:26:56.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 14:26:56.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-09-01 14:26:56.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-09-01 14:26:56.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:26:56.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:26:56.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:26:56.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:26:56.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:26:56.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:26:56.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:26:56.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:26:56.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:26:57.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:26:58.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:26:59.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:27:00.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:27:01.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:27:02.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:27:03.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:27:04.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:27:05.860 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:27:05.860 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:27:05.860 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:27:05.860 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:27:05.868 | 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

2025-09-01 14:27:05.871 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:27:05.960 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:27:06.040 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch262
2025-09-01 14:27:08.824 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 262/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.138s, data_time: 0.001s, total_loss: 8.6, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 4.0, cls_loss: 0.9, lr: 1.202e-03, size: 480, ETA: 1:54:41
2025-09-01 14:27:11.789 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 262/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 1.201e-03, size: 544, ETA: 1:54:38
2025-09-01 14:27:14.918 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 262/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.7, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.6, lr: 1.200e-03, size: 544, ETA: 1:54:35
2025-09-01 14:27:17.912 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 262/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 1.0, lr: 1.200e-03, size: 512, ETA: 1:54:31
2025-09-01 14:27:20.977 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 262/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.006s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.8, lr: 1.199e-03, size: 384, ETA: 1:54:28
2025-09-01 14:27:24.067 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 262/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.6, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 1.198e-03, size: 512, ETA: 1:54:25
2025-09-01 14:27:25.477 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:27:31.905 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:27:32.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:27:32.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5860
2025-09-01 14:27:32.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5125
2025-09-01 14:27:32.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3649
2025-09-01 14:27:32.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4878
2025-09-01 14:27:32.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:27:32.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:27:32.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-09-01 14:27:32.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-09-01 14:27:32.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-09-01 14:27:32.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-09-01 14:27:32.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:27:32.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:27:32.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:27:32.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:27:32.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:27:32.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:27:32.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:27:32.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:27:32.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:27:33.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:27:33.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:27:34.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:27:34.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:27:35.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:27:35.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:27:36.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:27:36.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:27:37.232 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:27:37.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:27:37.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:27:37.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:27:37.239 | 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

2025-09-01 14:27:37.240 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:27:37.322 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:27:37.404 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch263
2025-09-01 14:27:40.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 263/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 1.197e-03, size: 416, ETA: 1:54:20
2025-09-01 14:27:43.410 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 263/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.6, lr: 1.196e-03, size: 256, ETA: 1:54:17
2025-09-01 14:27:46.355 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 263/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.9, lr: 1.195e-03, size: 288, ETA: 1:54:13
2025-09-01 14:27:49.414 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 263/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 3.0, cls_loss: 0.8, lr: 1.194e-03, size: 320, ETA: 1:54:10
2025-09-01 14:27:52.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 263/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 1.0, lr: 1.194e-03, size: 448, ETA: 1:54:06
2025-09-01 14:27:55.421 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 263/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.7, lr: 1.193e-03, size: 320, ETA: 1:54:03
2025-09-01 14:27:56.739 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:28:03.160 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:28:03.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:28:04.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5446
2025-09-01 14:28:04.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.3699
2025-09-01 14:28:04.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2695
2025-09-01 14:28:04.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3947
2025-09-01 14:28:04.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:28:04.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:28:04.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-09-01 14:28:04.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-09-01 14:28:04.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.270
2025-09-01 14:28:04.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.395
2025-09-01 14:28:04.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:28:04.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:28:04.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:28:04.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:28:04.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:28:04.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:28:04.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:28:04.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:28:04.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:28:04.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:28:05.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:28:05.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:28:06.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:28:06.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:28:07.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:28:07.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:28:08.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:28:08.982 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:28:08.982 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.19
2025-09-01 14:28:08.982 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.39
2025-09-01 14:28:08.982 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:28:08.989 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.86 ms, Average inference time: 7.02 ms

2025-09-01 14:28:08.992 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:28:09.080 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:28:09.161 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch264
2025-09-01 14:28:12.131 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 264/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 1.192e-03, size: 448, ETA: 1:53:58
2025-09-01 14:28:15.069 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 264/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 1.191e-03, size: 288, ETA: 1:53:55
2025-09-01 14:28:17.922 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 264/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.140s, 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: 1.190e-03, size: 288, ETA: 1:53:51
2025-09-01 14:28:20.818 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 264/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.7, lr: 1.189e-03, size: 256, ETA: 1:53:47
2025-09-01 14:28:23.983 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 264/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.189e-03, size: 320, ETA: 1:53:44
2025-09-01 14:28:27.147 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 264/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.188e-03, size: 384, ETA: 1:53:41
2025-09-01 14:28:28.498 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:28:34.800 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:28:35.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:28:36.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6079
2025-09-01 14:28:36.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5138
2025-09-01 14:28:36.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3795
2025-09-01 14:28:36.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5004
2025-09-01 14:28:36.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:28:36.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:28:36.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-09-01 14:28:36.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.514
2025-09-01 14:28:36.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-09-01 14:28:36.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-09-01 14:28:36.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:28:36.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:28:36.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:28:36.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:28:36.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:28:36.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:28:36.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:28:36.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:28:36.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:28:37.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:28:38.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:28:39.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:28:40.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:28:41.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:28:42.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:28:43.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:28:44.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:28:45.023 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:28:45.023 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 14:28:45.023 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 14:28:45.023 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:28:45.031 | 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

2025-09-01 14:28:45.032 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:28:45.120 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:28:45.203 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch265
2025-09-01 14:28:48.084 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 265/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 2.7, cls_loss: 0.7, lr: 1.187e-03, size: 256, ETA: 1:53:36
2025-09-01 14:28:51.067 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 265/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.7, lr: 1.186e-03, size: 416, ETA: 1:53:33
2025-09-01 14:28:54.048 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 265/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, 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: 1.185e-03, size: 352, ETA: 1:53:29
2025-09-01 14:28:57.066 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 265/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.7, lr: 1.184e-03, size: 256, ETA: 1:53:26
2025-09-01 14:29:00.147 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 265/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.183e-03, size: 544, ETA: 1:53:23
2025-09-01 14:29:03.182 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 265/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.7, lr: 1.183e-03, size: 320, ETA: 1:53:19
2025-09-01 14:29:04.580 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:29:10.852 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:29:11.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:29:12.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5882
2025-09-01 14:29:12.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4998
2025-09-01 14:29:12.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3726
2025-09-01 14:29:12.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4869
2025-09-01 14:29:12.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:29:12.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:29:12.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-09-01 14:29:12.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-09-01 14:29:12.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.373
2025-09-01 14:29:12.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-09-01 14:29:12.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:29:12.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:29:12.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:29:12.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:29:12.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:29:12.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:29:12.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:29:12.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:29:12.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:29:13.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:29:14.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:29:15.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:29:16.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:29:17.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:29:17.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:29:18.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:29:19.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:29:20.434 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:29:20.434 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:29:20.434 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:29:20.434 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:29:20.442 | 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

2025-09-01 14:29:20.444 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:29:20.523 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:29:20.606 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch266
2025-09-01 14:29:23.413 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 266/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.8, lr: 1.181e-03, size: 288, ETA: 1:53:14
2025-09-01 14:29:26.460 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 266/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 1.181e-03, size: 320, ETA: 1:53:11
2025-09-01 14:29:29.570 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 266/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.180e-03, size: 320, ETA: 1:53:08
2025-09-01 14:29:32.713 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 266/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.6, lr: 1.179e-03, size: 288, ETA: 1:53:04
2025-09-01 14:29:35.852 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 266/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.6, lr: 1.178e-03, size: 512, ETA: 1:53:01
2025-09-01 14:29:38.920 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 266/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.9, lr: 1.177e-03, size: 480, ETA: 1:52:58
2025-09-01 14:29:40.310 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:29:46.452 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:29:47.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:29:48.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5953
2025-09-01 14:29:48.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5027
2025-09-01 14:29:48.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3945
2025-09-01 14:29:48.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4975
2025-09-01 14:29:48.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:29:48.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:29:48.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.595
2025-09-01 14:29:48.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-09-01 14:29:48.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-09-01 14:29:48.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.498
2025-09-01 14:29:48.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:29:48.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:29:48.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:29:48.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:29:48.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:29:48.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:29:48.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:29:48.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:29:48.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:29:49.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:29:49.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:29:50.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:29:51.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:29:52.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:29:52.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:29:53.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:29:54.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:29:55.335 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:29:55.335 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:29:55.336 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 14:29:55.336 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:29:55.343 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.89 ms, Average inference time: 7.10 ms

2025-09-01 14:29:55.350 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:29:55.432 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:29:55.514 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch267
2025-09-01 14:29:58.348 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 267/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 1.176e-03, size: 288, ETA: 1:52:53
2025-09-01 14:30:01.321 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 267/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 1.175e-03, size: 320, ETA: 1:52:50
2025-09-01 14:30:04.380 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 267/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.175e-03, size: 384, ETA: 1:52:46
2025-09-01 14:30:07.378 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 267/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, 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: 1.174e-03, size: 352, ETA: 1:52:43
2025-09-01 14:30:10.358 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 267/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.003s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 1.173e-03, size: 448, ETA: 1:52:39
2025-09-01 14:30:13.437 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 267/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.9, lr: 1.172e-03, size: 384, ETA: 1:52:36
2025-09-01 14:30:14.876 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:30:21.062 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:30:22.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:30:22.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5824
2025-09-01 14:30:22.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5029
2025-09-01 14:30:22.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3432
2025-09-01 14:30:22.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4762
2025-09-01 14:30:22.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:30:22.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:30:22.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-09-01 14:30:22.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-09-01 14:30:22.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.343
2025-09-01 14:30:22.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.476
2025-09-01 14:30:22.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:30:22.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:30:22.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:30:22.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:30:22.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:30:22.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:30:22.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:30:22.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:30:22.831 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:30:23.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:30:24.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:30:25.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:30:25.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:30:26.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:30:27.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:30:28.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:30:29.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:30:29.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:30:29.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 14:30:29.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:30:29.942 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:30:29.948 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.88 ms, Average inference time: 7.04 ms

2025-09-01 14:30:29.952 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:30:30.039 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:30:30.121 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch268
2025-09-01 14:30:33.010 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 268/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 1.171e-03, size: 544, ETA: 1:52:31
2025-09-01 14:30:36.101 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 268/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.6, lr: 1.170e-03, size: 480, ETA: 1:52:28
2025-09-01 14:30:39.157 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 268/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.9, lr: 1.170e-03, size: 320, ETA: 1:52:25
2025-09-01 14:30:42.240 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 268/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 1.169e-03, size: 480, ETA: 1:52:21
2025-09-01 14:30:45.333 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 268/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.5, lr: 1.168e-03, size: 288, ETA: 1:52:18
2025-09-01 14:30:48.294 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 268/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.167e-03, size: 288, ETA: 1:52:15
2025-09-01 14:30:49.571 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:30:55.767 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:30:57.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:30:58.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5876
2025-09-01 14:30:58.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5223
2025-09-01 14:30:58.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3579
2025-09-01 14:30:58.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4893
2025-09-01 14:30:58.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:30:58.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:30:58.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-09-01 14:30:58.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-09-01 14:30:58.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-09-01 14:30:58.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.489
2025-09-01 14:30:58.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:30:58.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:30:58.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:30:58.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:30:58.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:30:58.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:30:58.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:30:58.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:30:58.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:30:59.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:31:00.682 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:31:01.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:31:02.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:31:04.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:31:05.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:31:06.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:31:07.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:31:08.648 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:31:08.648 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:31:08.648 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:31:08.648 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:31:08.656 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.11 ms, Average NMS time: 0.89 ms, Average inference time: 7.00 ms

2025-09-01 14:31:08.657 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:31:08.795 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:31:08.871 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch269
2025-09-01 14:31:11.674 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 269/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.137s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 2.4, cls_loss: 0.6, lr: 1.166e-03, size: 384, ETA: 1:52:09
2025-09-01 14:31:14.572 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 269/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 1.165e-03, size: 480, ETA: 1:52:06
2025-09-01 14:31:17.569 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 269/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.164e-03, size: 576, ETA: 1:52:03
2025-09-01 14:31:20.695 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 269/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.5, cls_loss: 0.8, lr: 1.164e-03, size: 448, ETA: 1:51:59
2025-09-01 14:31:23.821 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 269/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 4.2, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.5, lr: 1.163e-03, size: 416, ETA: 1:51:56
2025-09-01 14:31:26.789 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 269/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 1.8, cls_loss: 0.6, lr: 1.162e-03, size: 576, ETA: 1:51:53
2025-09-01 14:31:28.265 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:31:34.398 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:31:35.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:31:35.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5666
2025-09-01 14:31:36.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4938
2025-09-01 14:31:36.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3237
2025-09-01 14:31:36.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4614
2025-09-01 14:31:36.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:31:36.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:31:36.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.567
2025-09-01 14:31:36.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-09-01 14:31:36.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.324
2025-09-01 14:31:36.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.461
2025-09-01 14:31:36.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:31:36.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:31:36.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:31:36.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:31:36.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:31:36.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:31:36.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:31:36.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:31:36.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:31:36.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:31:37.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:31:38.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:31:39.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:31:40.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:31:40.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:31:41.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:31:42.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:31:43.116 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:31:43.117 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:31:43.117 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 14:31:43.117 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:31:43.124 | 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

2025-09-01 14:31:43.125 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:31:43.212 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:31:43.294 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch270
2025-09-01 14:31:46.184 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 270/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 1.161e-03, size: 320, ETA: 1:51:48
2025-09-01 14:31:49.169 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 270/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.7, lr: 1.160e-03, size: 384, ETA: 1:51:45
2025-09-01 14:31:52.302 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 270/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 1.0, lr: 1.159e-03, size: 288, ETA: 1:51:41
2025-09-01 14:31:55.311 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 270/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.158e-03, size: 512, ETA: 1:51:38
2025-09-01 14:31:58.308 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 270/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 0.8, cls_loss: 0.5, lr: 1.158e-03, size: 352, ETA: 1:51:35
2025-09-01 14:32:01.586 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 270/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 1.157e-03, size: 256, ETA: 1:51:32
2025-09-01 14:32:02.953 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:32:09.195 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:32:10.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:32:11.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5775
2025-09-01 14:32:11.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4761
2025-09-01 14:32:11.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3679
2025-09-01 14:32:11.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4738
2025-09-01 14:32:11.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:32:11.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:32:11.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.577
2025-09-01 14:32:11.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.476
2025-09-01 14:32:11.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-09-01 14:32:11.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.474
2025-09-01 14:32:11.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:32:11.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:32:11.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:32:11.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:32:11.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:32:11.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:32:11.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:32:11.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:32:11.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:32:12.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:32:13.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:32:14.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:32:15.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:32:16.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:32:17.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:32:18.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:32:18.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:32:19.888 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:32:19.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:32:19.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 14:32:19.889 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:32:19.896 | 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: 7.22 ms

2025-09-01 14:32:19.898 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:32:19.985 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:32:20.067 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch271
2025-09-01 14:32:22.976 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 271/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.1, iou_loss: 1.8, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.5, lr: 1.156e-03, size: 576, ETA: 1:51:27
2025-09-01 14:32:26.096 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 271/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, 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: 1.155e-03, size: 256, ETA: 1:51:23
2025-09-01 14:32:29.142 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 271/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.7, cls_loss: 1.1, lr: 1.154e-03, size: 416, ETA: 1:51:20
2025-09-01 14:32:32.114 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 271/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 3.6, cls_loss: 0.6, lr: 1.153e-03, size: 352, ETA: 1:51:17
2025-09-01 14:32:35.149 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 271/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 1.152e-03, size: 256, ETA: 1:51:13
2025-09-01 14:32:38.275 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 271/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.152e-03, size: 352, ETA: 1:51:10
2025-09-01 14:32:39.629 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:32:45.766 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:32:47.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:32:47.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5635
2025-09-01 14:32:48.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4991
2025-09-01 14:32:48.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3711
2025-09-01 14:32:48.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4779
2025-09-01 14:32:48.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:32:48.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:32:48.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.563
2025-09-01 14:32:48.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-09-01 14:32:48.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-09-01 14:32:48.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-09-01 14:32:48.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:32:48.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:32:48.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:32:48.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:32:48.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:32:48.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:32:48.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:32:48.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:32:48.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:32:49.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:32:50.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:32:51.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:32:52.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:32:53.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:32:54.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:32:55.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:32:56.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:32:57.796 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:32:57.797 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:32:57.797 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:32:57.797 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:32:57.805 | 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: 7.17 ms

2025-09-01 14:32:57.806 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:32:57.942 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:32:58.017 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch272
2025-09-01 14:33:00.971 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 272/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 1.0, lr: 1.150e-03, size: 480, ETA: 1:51:05
2025-09-01 14:33:03.940 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 272/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.8, lr: 1.150e-03, size: 352, ETA: 1:51:02
2025-09-01 14:33:06.962 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 272/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 4.2, cls_loss: 0.9, lr: 1.149e-03, size: 288, ETA: 1:50:58
2025-09-01 14:33:09.893 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 272/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.8, lr: 1.148e-03, size: 320, ETA: 1:50:55
2025-09-01 14:33:13.055 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 272/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.147e-03, size: 576, ETA: 1:50:52
2025-09-01 14:33:16.123 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 272/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 1.146e-03, size: 256, ETA: 1:50:49
2025-09-01 14:33:17.468 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:33:23.646 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:33:24.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:33:25.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5872
2025-09-01 14:33:25.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5015
2025-09-01 14:33:25.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3838
2025-09-01 14:33:25.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4908
2025-09-01 14:33:25.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:33:25.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:33:25.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.587
2025-09-01 14:33:25.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-09-01 14:33:25.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-09-01 14:33:25.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.491
2025-09-01 14:33:25.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:33:25.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:33:25.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:33:25.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:33:25.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:33:25.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:33:25.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:33:25.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:33:25.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:33:26.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:33:27.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:33:28.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:33:29.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:33:30.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:33:31.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:33:33.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:33:34.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:33:34.994 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:33:34.995 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:33:34.995 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:33:34.995 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:33:35.003 | 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

2025-09-01 14:33:35.004 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:33:35.091 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:33:35.214 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch273
2025-09-01 14:33:38.012 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 273/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.139s, 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: 1.145e-03, size: 256, ETA: 1:50:43
2025-09-01 14:33:41.121 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 273/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 0.7, lr: 1.144e-03, size: 512, ETA: 1:50:40
2025-09-01 14:33:44.163 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 273/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.8, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 3.8, cls_loss: 0.8, lr: 1.144e-03, size: 288, ETA: 1:50:37
2025-09-01 14:33:47.109 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 273/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.8, lr: 1.143e-03, size: 416, ETA: 1:50:33
2025-09-01 14:33:50.179 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 273/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.6, lr: 1.142e-03, size: 480, ETA: 1:50:30
2025-09-01 14:33:53.146 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 273/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.141e-03, size: 352, ETA: 1:50:27
2025-09-01 14:33:54.471 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:34:00.620 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:34:01.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:34:02.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5671
2025-09-01 14:34:02.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4722
2025-09-01 14:34:02.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3656
2025-09-01 14:34:02.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4683
2025-09-01 14:34:02.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:34:02.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:34:02.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.567
2025-09-01 14:34:02.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.472
2025-09-01 14:34:02.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-09-01 14:34:02.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.468
2025-09-01 14:34:02.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:34:02.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:34:02.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:34:02.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:34:02.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:34:02.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:34:02.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:34:02.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:34:02.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:34:02.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:34:03.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:34:04.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:34:04.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:34:05.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:34:06.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:34:06.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:34:07.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:34:08.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:34:08.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:34:08.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 14:34:08.346 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:34:08.354 | 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

2025-09-01 14:34:08.355 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:34:08.484 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:34:08.604 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch274
2025-09-01 14:34:11.534 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 274/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, 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: 1.140e-03, size: 480, ETA: 1:50:22
2025-09-01 14:34:14.513 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 274/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 2.9, cls_loss: 0.9, lr: 1.139e-03, size: 320, ETA: 1:50:18
2025-09-01 14:34:17.592 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 274/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 1.0, lr: 1.138e-03, size: 416, ETA: 1:50:15
2025-09-01 14:34:20.551 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 274/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 1.138e-03, size: 448, ETA: 1:50:12
2025-09-01 14:34:23.624 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 274/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.137e-03, size: 544, ETA: 1:50:08
2025-09-01 14:34:26.607 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 274/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.8, lr: 1.136e-03, size: 544, ETA: 1:50:05
2025-09-01 14:34:27.961 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:34:34.471 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:34:35.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:34:35.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5800
2025-09-01 14:34:36.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5268
2025-09-01 14:34:36.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3749
2025-09-01 14:34:36.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4939
2025-09-01 14:34:36.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:34:36.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:34:36.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.580
2025-09-01 14:34:36.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-09-01 14:34:36.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-09-01 14:34:36.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-09-01 14:34:36.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:34:36.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:34:36.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:34:36.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:34:36.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:34:36.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:34:36.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:34:36.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:34:36.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:34:36.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:34:37.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:34:38.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:34:39.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:34:39.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:34:40.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:34:41.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:34:42.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:34:42.736 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:34:42.736 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:34:42.736 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:34:42.737 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:34:42.744 | 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

2025-09-01 14:34:42.745 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:34:42.831 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:34:42.915 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch275
2025-09-01 14:34:45.754 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 275/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.0, l1_loss: 0.8, conf_loss: 3.7, cls_loss: 0.8, lr: 1.135e-03, size: 288, ETA: 1:50:00
2025-09-01 14:34:48.699 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 275/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, 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: 1.134e-03, size: 352, ETA: 1:49:57
2025-09-01 14:34:51.707 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 275/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.7, lr: 1.133e-03, size: 320, ETA: 1:49:53
2025-09-01 14:34:54.653 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 275/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 1.6, cls_loss: 0.6, lr: 1.132e-03, size: 352, ETA: 1:49:50
2025-09-01 14:34:57.643 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 275/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 3.8, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 1.132e-03, size: 256, ETA: 1:49:46
2025-09-01 14:35:00.794 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 275/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 1.1, lr: 1.131e-03, size: 320, ETA: 1:49:43
2025-09-01 14:35:02.103 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:35:08.490 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:35:09.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:35:09.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5760
2025-09-01 14:35:09.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5180
2025-09-01 14:35:09.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3452
2025-09-01 14:35:09.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4797
2025-09-01 14:35:09.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:35:09.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:35:09.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-09-01 14:35:09.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.518
2025-09-01 14:35:09.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.345
2025-09-01 14:35:09.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.480
2025-09-01 14:35:09.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:35:09.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:35:09.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:35:09.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:35:09.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:35:09.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:35:09.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:35:09.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:35:09.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:35:10.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:35:11.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:35:11.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:35:12.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:35:12.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:35:13.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:35:14.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:35:14.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:35:15.212 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:35:15.212 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:35:15.212 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:35:15.212 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:35:15.219 | 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

2025-09-01 14:35:15.220 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:35:15.372 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:35:15.446 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch276
2025-09-01 14:35:18.304 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 276/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, 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: 1.130e-03, size: 544, ETA: 1:49:38
2025-09-01 14:35:21.397 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 276/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 1.129e-03, size: 480, ETA: 1:49:35
2025-09-01 14:35:24.395 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 276/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 1.128e-03, size: 480, ETA: 1:49:32
2025-09-01 14:35:27.452 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 276/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 1.7, cls_loss: 0.6, lr: 1.127e-03, size: 256, ETA: 1:49:28
2025-09-01 14:35:30.440 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 276/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.6, lr: 1.127e-03, size: 416, ETA: 1:49:25
2025-09-01 14:35:33.495 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 276/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.126e-03, size: 320, ETA: 1:49:22
2025-09-01 14:35:34.788 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:35:40.979 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:35:42.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:35:42.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5889
2025-09-01 14:35:43.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5046
2025-09-01 14:35:43.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3844
2025-09-01 14:35:43.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4926
2025-09-01 14:35:43.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:35:43.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:35:43.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-09-01 14:35:43.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-09-01 14:35:43.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-09-01 14:35:43.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-09-01 14:35:43.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:35:43.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:35:43.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:35:43.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:35:43.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:35:43.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:35:43.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:35:43.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:35:43.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:35:44.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:35:45.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:35:46.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:35:47.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:35:48.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:35:49.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:35:50.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:35:51.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:35:52.161 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:35:52.161 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 14:35:52.161 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:35:52.161 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:35:52.168 | 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

2025-09-01 14:35:52.176 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:35:52.266 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:35:52.412 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch277
2025-09-01 14:35:55.285 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 277/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 1.0, lr: 1.125e-03, size: 256, ETA: 1:49:17
2025-09-01 14:35:58.270 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 277/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.7, lr: 1.124e-03, size: 320, ETA: 1:49:13
2025-09-01 14:36:01.205 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 277/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, 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: 1.123e-03, size: 576, ETA: 1:49:10
2025-09-01 14:36:04.263 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 277/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, 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: 1.122e-03, size: 448, ETA: 1:49:07
2025-09-01 14:36:07.216 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 277/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.003s, total_loss: 4.0, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 1.121e-03, size: 288, ETA: 1:49:03
2025-09-01 14:36:10.318 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 277/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.120e-03, size: 448, ETA: 1:49:00
2025-09-01 14:36:11.732 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:36:18.183 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:36:19.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:36:19.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5849
2025-09-01 14:36:19.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5071
2025-09-01 14:36:19.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3344
2025-09-01 14:36:19.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4755
2025-09-01 14:36:19.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:36:19.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:36:19.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.585
2025-09-01 14:36:19.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-09-01 14:36:19.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.334
2025-09-01 14:36:19.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.475
2025-09-01 14:36:19.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:36:19.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:36:19.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:36:19.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:36:19.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:36:19.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:36:19.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:36:19.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:36:19.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:36:20.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:36:20.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:36:21.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:36:22.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:36:22.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:36:23.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:36:23.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:36:24.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:36:25.295 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:36:25.295 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:36:25.295 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:36:25.295 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:36:25.302 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.91 ms, Average inference time: 7.23 ms

2025-09-01 14:36:25.304 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:36:25.433 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:36:25.518 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch278
2025-09-01 14:36:28.334 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 278/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 0.9, cls_loss: 0.6, lr: 1.119e-03, size: 512, ETA: 1:48:55
2025-09-01 14:36:31.419 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 278/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 1.119e-03, size: 544, ETA: 1:48:52
2025-09-01 14:36:34.547 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 278/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.8, lr: 1.118e-03, size: 288, ETA: 1:48:48
2025-09-01 14:36:37.535 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 278/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 1.2, lr: 1.117e-03, size: 352, ETA: 1:48:45
2025-09-01 14:36:40.531 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 278/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.5, lr: 1.116e-03, size: 512, ETA: 1:48:42
2025-09-01 14:36:43.639 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 278/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 9.3, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 4.7, cls_loss: 1.0, lr: 1.115e-03, size: 288, ETA: 1:48:39
2025-09-01 14:36:44.975 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:36:51.289 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:36:52.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:36:53.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5794
2025-09-01 14:36:53.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4902
2025-09-01 14:36:53.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2954
2025-09-01 14:36:53.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4550
2025-09-01 14:36:53.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:36:53.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:36:53.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-09-01 14:36:53.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-09-01 14:36:53.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.295
2025-09-01 14:36:53.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.455
2025-09-01 14:36:53.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:36:53.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:36:53.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:36:53.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:36:53.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:36:53.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:36:53.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:36:53.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:36:53.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:36:54.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:36:55.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:36:56.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:36:57.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:36:58.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:36:59.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:36:59.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:37:00.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:37:01.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:37:01.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:37:01.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 14:37:01.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:37:01.770 | 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

2025-09-01 14:37:01.771 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:37:01.851 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:37:01.934 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch279
2025-09-01 14:37:04.820 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 279/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.6, lr: 1.114e-03, size: 512, ETA: 1:48:34
2025-09-01 14:37:07.984 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 279/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.8, lr: 1.113e-03, size: 352, ETA: 1:48:30
2025-09-01 14:37:11.049 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 279/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.8, lr: 1.113e-03, size: 352, ETA: 1:48:27
2025-09-01 14:37:14.152 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 279/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, 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: 1.112e-03, size: 288, ETA: 1:48:24
2025-09-01 14:37:17.175 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 279/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.111e-03, size: 576, ETA: 1:48:21
2025-09-01 14:37:20.338 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 279/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.5, lr: 1.110e-03, size: 352, ETA: 1:48:17
2025-09-01 14:37:21.690 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:37:27.749 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:37:28.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:37:28.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5529
2025-09-01 14:37:28.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4728
2025-09-01 14:37:28.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3202
2025-09-01 14:37:28.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4487
2025-09-01 14:37:28.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:37:28.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:37:28.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.553
2025-09-01 14:37:28.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.473
2025-09-01 14:37:28.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.320
2025-09-01 14:37:28.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.449
2025-09-01 14:37:28.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:37:28.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:37:28.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:37:28.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:37:28.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:37:28.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:37:28.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:37:28.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:37:28.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:37:29.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:37:29.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:37:29.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:37:30.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:37:30.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:37:30.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:37:31.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:37:31.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:37:31.977 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:37:31.977 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:37:31.977 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 14:37:31.978 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:37:31.984 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.85 ms, Average inference time: 7.03 ms

2025-09-01 14:37:31.984 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:37:32.071 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:37:32.152 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch280
2025-09-01 14:37:35.116 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 280/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 3.4, cls_loss: 0.6, lr: 1.109e-03, size: 576, ETA: 1:48:13
2025-09-01 14:37:38.287 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 280/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.5, lr: 1.108e-03, size: 448, ETA: 1:48:09
2025-09-01 14:37:41.335 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 280/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.6, lr: 1.107e-03, size: 576, ETA: 1:48:06
2025-09-01 14:37:44.504 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 280/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.157s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.107e-03, size: 256, ETA: 1:48:03
2025-09-01 14:37:47.656 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 280/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, 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: 1.106e-03, size: 512, ETA: 1:48:00
2025-09-01 14:37:50.691 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 280/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 1.2, lr: 1.105e-03, size: 320, ETA: 1:47:57
2025-09-01 14:37:52.057 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:37:58.374 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:37:58.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:37:59.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5850
2025-09-01 14:37:59.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4931
2025-09-01 14:37:59.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3467
2025-09-01 14:37:59.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4749
2025-09-01 14:37:59.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:37:59.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:37:59.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.585
2025-09-01 14:37:59.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-09-01 14:37:59.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.347
2025-09-01 14:37:59.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.475
2025-09-01 14:37:59.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:37:59.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:37:59.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:37:59.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:37:59.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:37:59.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:37:59.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:37:59.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:37:59.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:37:59.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:38:00.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:38:00.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:38:01.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:38:02.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:38:02.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:38:03.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:38:03.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:38:04.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:38:04.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:38:04.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 14:38:04.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:38:04.022 | 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: 7.17 ms

2025-09-01 14:38:04.024 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:38:04.115 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:38:04.197 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch281
2025-09-01 14:38:07.125 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 281/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 9.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 4.4, cls_loss: 0.9, lr: 1.104e-03, size: 544, ETA: 1:47:52
2025-09-01 14:38:10.072 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 281/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, 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: 1.103e-03, size: 384, ETA: 1:47:48
2025-09-01 14:38:13.157 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 281/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.102e-03, size: 576, ETA: 1:47:45
2025-09-01 14:38:16.287 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 281/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.9, lr: 1.101e-03, size: 480, ETA: 1:47:42
2025-09-01 14:38:19.394 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 281/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.7, lr: 1.100e-03, size: 480, ETA: 1:47:39
2025-09-01 14:38:22.455 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 281/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 1.0, lr: 1.100e-03, size: 384, ETA: 1:47:35
2025-09-01 14:38:23.797 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:38:29.910 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:38:31.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:38:32.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5774
2025-09-01 14:38:32.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5007
2025-09-01 14:38:32.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3491
2025-09-01 14:38:32.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4757
2025-09-01 14:38:32.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:38:32.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:38:32.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.577
2025-09-01 14:38:32.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-09-01 14:38:32.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.349
2025-09-01 14:38:32.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.476
2025-09-01 14:38:32.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:38:32.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:38:32.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:38:32.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:38:32.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:38:32.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:38:32.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:38:32.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:38:32.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:38:34.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:38:35.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:38:37.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:38:38.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:38:39.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:38:41.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:38:42.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:38:44.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:38:45.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:38:45.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:38:45.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:38:45.415 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:38:45.425 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.06 ms, Average NMS time: 0.95 ms, Average inference time: 7.01 ms

2025-09-01 14:38:45.436 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:38:45.519 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:38:45.602 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch282
2025-09-01 14:38:48.527 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 282/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, 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: 1.099e-03, size: 288, ETA: 1:47:30
2025-09-01 14:38:51.593 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 282/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.5, lr: 1.098e-03, size: 288, ETA: 1:47:27
2025-09-01 14:38:54.677 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 282/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.2, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 1.097e-03, size: 512, ETA: 1:47:24
2025-09-01 14:38:57.646 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 282/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 4.3, cls_loss: 0.7, lr: 1.096e-03, size: 480, ETA: 1:47:20
2025-09-01 14:39:00.628 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 282/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.095e-03, size: 352, ETA: 1:47:17
2025-09-01 14:39:03.672 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 282/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 1.094e-03, size: 352, ETA: 1:47:14
2025-09-01 14:39:05.003 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:39:11.253 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:39:12.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:39:13.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5987
2025-09-01 14:39:13.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5093
2025-09-01 14:39:13.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3607
2025-09-01 14:39:13.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4896
2025-09-01 14:39:13.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:39:13.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:39:13.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-09-01 14:39:13.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-09-01 14:39:13.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-09-01 14:39:13.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-09-01 14:39:13.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:39:13.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:39:13.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:39:13.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:39:13.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:39:13.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:39:13.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:39:13.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:39:13.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:39:14.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:39:15.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:39:16.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:39:17.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:39:18.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:39:19.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:39:20.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:39:21.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:39:22.009 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:39:22.009 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 14:39:22.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:39:22.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:39:22.017 | 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

2025-09-01 14:39:22.019 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:39:22.142 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:39:22.258 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch283
2025-09-01 14:39:25.235 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 283/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.7, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.6, lr: 1.093e-03, size: 256, ETA: 1:47:09
2025-09-01 14:39:28.177 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 283/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 1.2, lr: 1.092e-03, size: 512, ETA: 1:47:06
2025-09-01 14:39:31.388 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 283/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 9.2, iou_loss: 3.5, l1_loss: 1.3, conf_loss: 3.6, cls_loss: 0.8, lr: 1.092e-03, size: 320, ETA: 1:47:02
2025-09-01 14:39:34.409 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 283/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, 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: 1.091e-03, size: 512, ETA: 1:46:59
2025-09-01 14:39:37.483 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 283/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.090e-03, size: 448, ETA: 1:46:56
2025-09-01 14:39:40.546 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 283/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.6, lr: 1.089e-03, size: 544, ETA: 1:46:53
2025-09-01 14:39:41.888 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:39:48.008 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:39:48.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:39:48.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5173
2025-09-01 14:39:48.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4391
2025-09-01 14:39:48.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2716
2025-09-01 14:39:48.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4093
2025-09-01 14:39:48.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:39:48.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:39:48.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 14:39:48.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.439
2025-09-01 14:39:48.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.272
2025-09-01 14:39:48.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.409
2025-09-01 14:39:48.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:39:48.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:39:48.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:39:48.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:39:48.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:39:48.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:39:48.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:39:48.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:39:48.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:39:49.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:39:49.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:39:49.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:39:49.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:39:50.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:39:50.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:39:50.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:39:50.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:39:51.167 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:39:51.168 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.18
2025-09-01 14:39:51.168 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-09-01 14:39:51.168 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:39:51.174 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.34 ms, Average NMS time: 0.78 ms, Average inference time: 7.12 ms

2025-09-01 14:39:51.176 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:39:51.265 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:39:51.342 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch284
2025-09-01 14:39:54.235 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 284/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.6, lr: 1.088e-03, size: 256, ETA: 1:46:48
2025-09-01 14:39:57.143 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 284/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.7, iou_loss: 1.8, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.5, lr: 1.087e-03, size: 480, ETA: 1:46:44
2025-09-01 14:40:00.215 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 284/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.2, l1_loss: 1.3, conf_loss: 2.6, cls_loss: 0.7, lr: 1.086e-03, size: 288, ETA: 1:46:41
2025-09-01 14:40:03.294 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 284/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.2, cls_loss: 0.8, lr: 1.086e-03, size: 576, ETA: 1:46:38
2025-09-01 14:40:06.476 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 284/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.7, lr: 1.085e-03, size: 384, ETA: 1:46:35
2025-09-01 14:40:09.489 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 284/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 1.084e-03, size: 320, ETA: 1:46:31
2025-09-01 14:40:10.857 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:40:17.245 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:40:17.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:40:18.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5944
2025-09-01 14:40:18.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5113
2025-09-01 14:40:18.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3640
2025-09-01 14:40:18.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4899
2025-09-01 14:40:18.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:40:18.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:40:18.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.594
2025-09-01 14:40:18.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-09-01 14:40:18.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-09-01 14:40:18.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-09-01 14:40:18.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:40:18.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:40:18.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:40:18.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:40:18.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:40:18.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:40:18.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:40:18.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:40:18.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:40:19.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:40:19.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:40:20.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:40:20.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:40:21.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:40:21.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:40:22.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:40:22.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:40:23.500 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:40:23.501 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 14:40:23.501 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:40:23.501 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:40:23.508 | 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

2025-09-01 14:40:23.509 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:40:23.596 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:40:23.677 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch285
2025-09-01 14:40:26.560 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 285/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.083e-03, size: 448, ETA: 1:46:26
2025-09-01 14:40:29.557 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 285/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, 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: 1.082e-03, size: 384, ETA: 1:46:23
2025-09-01 14:40:32.659 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 285/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 1.081e-03, size: 544, ETA: 1:46:20
2025-09-01 14:40:35.768 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 285/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.7, lr: 1.080e-03, size: 288, ETA: 1:46:17
2025-09-01 14:40:38.917 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 285/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.157s, data_time: 0.005s, total_loss: 4.9, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 2.3, cls_loss: 0.5, lr: 1.080e-03, size: 416, ETA: 1:46:13
2025-09-01 14:40:41.925 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 285/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.9, lr: 1.079e-03, size: 416, ETA: 1:46:10
2025-09-01 14:40:43.303 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:40:49.588 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:40:50.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:40:50.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5684
2025-09-01 14:40:50.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4769
2025-09-01 14:40:50.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3404
2025-09-01 14:40:50.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4619
2025-09-01 14:40:50.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:40:50.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:40:50.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.568
2025-09-01 14:40:50.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.477
2025-09-01 14:40:50.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-09-01 14:40:50.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.462
2025-09-01 14:40:50.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:40:50.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:40:50.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:40:50.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:40:50.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:40:50.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:40:50.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:40:50.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:40:50.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:40:51.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:40:51.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:40:51.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:40:52.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:40:52.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:40:53.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:40:53.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:40:54.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:40:54.709 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:40:54.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:40:54.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 14:40:54.710 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:40:54.716 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.84 ms, Average inference time: 7.01 ms

2025-09-01 14:40:54.718 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:40:54.803 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:40:54.884 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch286
2025-09-01 14:40:57.886 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 286/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.7, lr: 1.078e-03, size: 384, ETA: 1:46:05
2025-09-01 14:41:00.937 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 286/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 1.077e-03, size: 352, ETA: 1:46:02
2025-09-01 14:41:04.026 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 286/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, 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: 1.076e-03, size: 352, ETA: 1:45:59
2025-09-01 14:41:06.978 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 286/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.7, lr: 1.075e-03, size: 320, ETA: 1:45:55
2025-09-01 14:41:09.947 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 286/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.003s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.8, lr: 1.074e-03, size: 320, ETA: 1:45:52
2025-09-01 14:41:12.907 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 286/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.8, cls_loss: 0.7, lr: 1.074e-03, size: 384, ETA: 1:45:49
2025-09-01 14:41:14.272 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:41:20.394 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:41:20.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:41:21.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5358
2025-09-01 14:41:21.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4611
2025-09-01 14:41:21.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.1495
2025-09-01 14:41:21.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.3821
2025-09-01 14:41:21.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:41:21.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:41:21.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-09-01 14:41:21.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.461
2025-09-01 14:41:21.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.149
2025-09-01 14:41:21.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.382
2025-09-01 14:41:21.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:41:21.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:41:21.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:41:21.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:41:21.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:41:21.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:41:21.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:41:21.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:41:21.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:41:21.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:41:22.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:41:22.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:41:22.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:41:23.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:41:23.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:41:23.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:41:24.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:41:24.613 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:41:24.613 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.20
2025-09-01 14:41:24.613 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.38
2025-09-01 14:41:24.613 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:41:24.619 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.83 ms, Average inference time: 7.09 ms

2025-09-01 14:41:24.621 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:41:24.729 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:41:24.806 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch287
2025-09-01 14:41:27.725 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 287/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.6, lr: 1.072e-03, size: 288, ETA: 1:45:44
2025-09-01 14:41:30.757 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 287/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.9, lr: 1.072e-03, size: 384, ETA: 1:45:40
2025-09-01 14:41:33.698 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 287/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.9, lr: 1.071e-03, size: 480, ETA: 1:45:37
2025-09-01 14:41:36.643 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 287/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, 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: 1.070e-03, size: 448, ETA: 1:45:34
2025-09-01 14:41:39.731 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 287/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.8, lr: 1.069e-03, size: 288, ETA: 1:45:30
2025-09-01 14:41:42.755 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 287/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.8, lr: 1.068e-03, size: 480, ETA: 1:45:27
2025-09-01 14:41:44.157 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:41:50.388 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:41:51.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:41:52.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5716
2025-09-01 14:41:52.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4804
2025-09-01 14:41:52.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3578
2025-09-01 14:41:52.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4700
2025-09-01 14:41:52.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:41:52.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:41:52.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.572
2025-09-01 14:41:52.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.480
2025-09-01 14:41:52.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-09-01 14:41:52.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.470
2025-09-01 14:41:52.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:41:52.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:41:52.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:41:52.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:41:52.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:41:52.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:41:52.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:41:52.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:41:52.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:41:53.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:41:54.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:41:55.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:41:56.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:41:57.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:41:58.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:41:59.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:41:59.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:42:00.933 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:42:00.933 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 14:42:00.933 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 14:42:00.933 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:42:00.940 | 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

2025-09-01 14:42:00.942 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:42:01.028 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:42:01.109 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch288
2025-09-01 14:42:04.025 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 288/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.6, lr: 1.067e-03, size: 512, ETA: 1:45:22
2025-09-01 14:42:07.000 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 288/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.6, lr: 1.066e-03, size: 544, ETA: 1:45:19
2025-09-01 14:42:10.074 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 288/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 1.066e-03, size: 288, ETA: 1:45:16
2025-09-01 14:42:13.146 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 288/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 1.065e-03, size: 256, ETA: 1:45:12
2025-09-01 14:42:16.291 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 288/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 1.064e-03, size: 576, ETA: 1:45:09
2025-09-01 14:42:19.433 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 288/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.7, lr: 1.063e-03, size: 352, ETA: 1:45:06
2025-09-01 14:42:20.763 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:42:26.933 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:42:27.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:42:28.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5944
2025-09-01 14:42:28.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5147
2025-09-01 14:42:28.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3728
2025-09-01 14:42:28.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4939
2025-09-01 14:42:28.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:42:28.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:42:28.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.594
2025-09-01 14:42:28.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 14:42:28.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.373
2025-09-01 14:42:28.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-09-01 14:42:28.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:42:28.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:42:28.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:42:28.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:42:28.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:42:28.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:42:28.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:42:28.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:42:28.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:42:29.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:42:30.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:42:31.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:42:32.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:42:32.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:42:33.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:42:34.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:42:35.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:42:36.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:42:36.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:42:36.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:42:36.031 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:42:36.039 | 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: 7.10 ms

2025-09-01 14:42:36.040 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:42:36.125 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:42:36.208 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch289
2025-09-01 14:42:39.084 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 289/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 1.062e-03, size: 544, ETA: 1:45:01
2025-09-01 14:42:42.142 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 289/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.6, cls_loss: 1.0, lr: 1.061e-03, size: 384, ETA: 1:44:58
2025-09-01 14:42:45.260 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 289/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.060e-03, size: 320, ETA: 1:44:55
2025-09-01 14:42:48.236 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 289/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.6, lr: 1.060e-03, size: 288, ETA: 1:44:51
2025-09-01 14:42:51.201 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 289/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 2.2, cls_loss: 0.5, lr: 1.059e-03, size: 384, ETA: 1:44:48
2025-09-01 14:42:54.201 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 289/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, 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: 1.058e-03, size: 544, ETA: 1:44:45
2025-09-01 14:42:55.525 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:43:01.828 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:43:02.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:43:02.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5559
2025-09-01 14:43:02.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4772
2025-09-01 14:43:02.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3090
2025-09-01 14:43:02.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4473
2025-09-01 14:43:02.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:43:02.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:43:02.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.556
2025-09-01 14:43:02.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.477
2025-09-01 14:43:02.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.309
2025-09-01 14:43:02.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.447
2025-09-01 14:43:02.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:43:02.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:43:02.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:43:02.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:43:02.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:43:02.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:43:02.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:43:02.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:43:02.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:43:03.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:43:04.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:43:04.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:43:05.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:43:05.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:43:06.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:43:06.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:43:07.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:43:07.604 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:43:07.604 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 14:43:07.605 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 14:43:07.605 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:43:07.618 | 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

2025-09-01 14:43:07.620 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:43:07.735 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:43:07.811 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch290
2025-09-01 14:43:10.807 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 290/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.7, lr: 1.057e-03, size: 352, ETA: 1:44:40
2025-09-01 14:43:13.875 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 290/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.8, lr: 1.056e-03, size: 576, ETA: 1:44:37
2025-09-01 14:43:17.046 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 290/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.7, lr: 1.055e-03, size: 352, ETA: 1:44:34
2025-09-01 14:43:20.081 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 290/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.054e-03, size: 352, ETA: 1:44:30
2025-09-01 14:43:23.108 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 290/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.8, lr: 1.054e-03, size: 448, ETA: 1:44:27
2025-09-01 14:43:26.074 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 290/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 2.5, cls_loss: 0.5, lr: 1.053e-03, size: 448, ETA: 1:44:24
2025-09-01 14:43:27.479 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:43:33.813 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:43:34.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:43:35.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5912
2025-09-01 14:43:35.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4995
2025-09-01 14:43:35.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3545
2025-09-01 14:43:35.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4817
2025-09-01 14:43:35.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:43:35.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:43:35.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.591
2025-09-01 14:43:35.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-09-01 14:43:35.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.355
2025-09-01 14:43:35.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.482
2025-09-01 14:43:35.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:43:35.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:43:35.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:43:35.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:43:35.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:43:35.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:43:35.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:43:35.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:43:35.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:43:36.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:43:36.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:43:37.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:43:38.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:43:39.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:43:39.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:43:40.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:43:41.259 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:43:42.001 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:43:42.001 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:43:42.002 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:43:42.002 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:43:42.009 | 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

2025-09-01 14:43:42.010 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:43:42.100 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:43:42.184 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch291
2025-09-01 14:43:45.244 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 291/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.7, lr: 1.052e-03, size: 352, ETA: 1:44:19
2025-09-01 14:43:48.147 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 291/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.7, lr: 1.051e-03, size: 288, ETA: 1:44:15
2025-09-01 14:43:51.200 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 291/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.7, lr: 1.050e-03, size: 576, ETA: 1:44:12
2025-09-01 14:43:54.267 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 291/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 1.049e-03, size: 480, ETA: 1:44:09
2025-09-01 14:43:57.282 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 291/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 1.048e-03, size: 512, ETA: 1:44:06
2025-09-01 14:44:00.250 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 291/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.3Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.047e-03, size: 384, ETA: 1:44:02
2025-09-01 14:44:01.638 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:44:07.813 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:44:09.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:44:10.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5835
2025-09-01 14:44:10.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4902
2025-09-01 14:44:10.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3582
2025-09-01 14:44:10.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4773
2025-09-01 14:44:10.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:44:10.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:44:10.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.583
2025-09-01 14:44:10.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-09-01 14:44:10.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-09-01 14:44:10.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.477
2025-09-01 14:44:10.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:44:10.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:44:10.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:44:10.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:44:10.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:44:10.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:44:10.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:44:10.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:44:10.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:44:11.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:44:12.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:44:13.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:44:14.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:44:15.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:44:17.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:44:18.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:44:19.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:44:20.504 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:44:20.504 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:44:20.504 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:44:20.504 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:44:20.512 | 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: 7.17 ms

2025-09-01 14:44:20.513 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:44:20.592 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:44:20.674 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch292
2025-09-01 14:44:23.621 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 292/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, 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: 1.046e-03, size: 448, ETA: 1:43:58
2025-09-01 14:44:26.671 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 292/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 1.045e-03, size: 384, ETA: 1:43:54
2025-09-01 14:44:29.586 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 292/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 1.045e-03, size: 352, ETA: 1:43:51
2025-09-01 14:44:32.604 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 292/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 1.6, cls_loss: 0.7, lr: 1.044e-03, size: 512, ETA: 1:43:48
2025-09-01 14:44:35.592 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 292/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.5, lr: 1.043e-03, size: 352, ETA: 1:43:44
2025-09-01 14:44:38.518 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 292/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 1.042e-03, size: 512, ETA: 1:43:41
2025-09-01 14:44:39.909 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:44:46.082 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:44:46.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:44:47.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5876
2025-09-01 14:44:47.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4860
2025-09-01 14:44:47.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3868
2025-09-01 14:44:47.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4868
2025-09-01 14:44:47.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:44:47.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:44:47.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-09-01 14:44:47.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-09-01 14:44:47.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-09-01 14:44:47.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-09-01 14:44:47.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:44:47.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:44:47.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:44:47.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:44:47.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:44:47.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:44:47.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:44:47.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:44:47.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:44:47.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:44:48.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:44:48.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:44:48.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:44:49.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:44:50.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:44:50.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:44:51.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:44:51.562 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:44:51.563 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:44:51.563 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:44:51.563 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:44:51.570 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.87 ms, Average inference time: 7.12 ms

2025-09-01 14:44:51.571 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:44:51.661 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:44:51.746 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch293
2025-09-01 14:44:54.585 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 293/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 6.3, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 3.2, cls_loss: 0.7, lr: 1.041e-03, size: 480, ETA: 1:43:36
2025-09-01 14:44:57.704 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 293/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.8, lr: 1.040e-03, size: 256, ETA: 1:43:33
2025-09-01 14:45:00.754 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 293/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 2.8, cls_loss: 0.6, lr: 1.039e-03, size: 576, ETA: 1:43:29
2025-09-01 14:45:03.839 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 293/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, 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: 1.039e-03, size: 384, ETA: 1:43:26
2025-09-01 14:45:07.001 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 293/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.6, lr: 1.038e-03, size: 480, ETA: 1:43:23
2025-09-01 14:45:09.985 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 293/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 1.037e-03, size: 288, ETA: 1:43:20
2025-09-01 14:45:11.377 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:45:17.799 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:45:18.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:45:19.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5901
2025-09-01 14:45:19.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5078
2025-09-01 14:45:19.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3716
2025-09-01 14:45:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4898
2025-09-01 14:45:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:45:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:45:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.590
2025-09-01 14:45:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.508
2025-09-01 14:45:19.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-09-01 14:45:19.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-09-01 14:45:19.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:45:19.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:45:19.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:45:19.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:45:19.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:45:19.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:45:19.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:45:19.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:45:19.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:45:20.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:45:21.535 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:45:22.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:45:23.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:45:24.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:45:25.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:45:26.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:45:27.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:45:27.948 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:45:27.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:45:27.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:45:27.949 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:45:27.956 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.42 ms, Average NMS time: 0.94 ms, Average inference time: 7.36 ms

2025-09-01 14:45:27.959 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:45:28.043 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:45:28.124 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch294
2025-09-01 14:45:31.070 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 294/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 1.036e-03, size: 384, ETA: 1:43:15
2025-09-01 14:45:34.071 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 294/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 16.8, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 16.8, cls_loss: 0.0, lr: 1.035e-03, size: 416, ETA: 1:43:12
2025-09-01 14:45:37.165 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 294/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 1.034e-03, size: 352, ETA: 1:43:08
2025-09-01 14:45:40.245 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 294/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.6, lr: 1.033e-03, size: 512, ETA: 1:43:05
2025-09-01 14:45:43.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 294/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 1.033e-03, size: 544, ETA: 1:43:02
2025-09-01 14:45:46.362 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 294/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 1.1, lr: 1.032e-03, size: 448, ETA: 1:42:59
2025-09-01 14:45:47.698 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:45:53.965 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:45:54.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:45:55.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5880
2025-09-01 14:45:55.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5322
2025-09-01 14:45:55.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3384
2025-09-01 14:45:55.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4862
2025-09-01 14:45:55.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:45:55.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:45:55.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-09-01 14:45:55.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-09-01 14:45:55.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.338
2025-09-01 14:45:55.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.486
2025-09-01 14:45:55.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:45:55.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:45:55.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:45:55.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:45:55.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:45:55.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:45:55.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:45:55.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:45:55.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:45:56.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:45:56.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:45:57.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:45:58.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:45:59.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:45:59.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:46:00.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:46:01.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:46:01.738 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:46:01.738 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:46:01.739 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:46:01.739 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:46:01.746 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.36 ms, Average NMS time: 0.89 ms, Average inference time: 7.24 ms

2025-09-01 14:46:01.748 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:46:01.880 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:46:01.953 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch295
2025-09-01 14:46:04.954 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 295/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 1.031e-03, size: 256, ETA: 1:42:54
2025-09-01 14:46:08.065 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 295/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 1.030e-03, size: 448, ETA: 1:42:51
2025-09-01 14:46:11.163 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 295/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, 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: 1.029e-03, size: 512, ETA: 1:42:48
2025-09-01 14:46:14.180 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 295/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.028e-03, size: 320, ETA: 1:42:44
2025-09-01 14:46:17.288 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 295/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.9, lr: 1.027e-03, size: 512, ETA: 1:42:41
2025-09-01 14:46:20.381 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 295/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 8.9, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 4.2, cls_loss: 0.8, lr: 1.027e-03, size: 480, ETA: 1:42:38
2025-09-01 14:46:21.697 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:46:27.874 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:46:29.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:46:30.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5562
2025-09-01 14:46:30.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4820
2025-09-01 14:46:30.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2584
2025-09-01 14:46:30.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4322
2025-09-01 14:46:30.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:46:30.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:46:30.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.556
2025-09-01 14:46:30.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.482
2025-09-01 14:46:30.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.258
2025-09-01 14:46:30.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.432
2025-09-01 14:46:30.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:46:30.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:46:30.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:46:30.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:46:30.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:46:30.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:46:30.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:46:30.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:46:30.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:46:31.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:46:32.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:46:34.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:46:35.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:46:36.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:46:37.488 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:46:38.647 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:46:39.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:46:40.926 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:46:40.926 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 14:46:40.926 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 14:46:40.926 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:46:40.934 | 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

2025-09-01 14:46:40.939 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:46:41.026 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:46:41.107 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch296
2025-09-01 14:46:43.881 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 296/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.136s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 1.025e-03, size: 320, ETA: 1:42:33
2025-09-01 14:46:46.900 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 296/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 1.025e-03, size: 416, ETA: 1:42:29
2025-09-01 14:46:49.866 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 296/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.5, lr: 1.024e-03, size: 416, ETA: 1:42:26
2025-09-01 14:46:53.011 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 296/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 1.023e-03, size: 320, ETA: 1:42:23
2025-09-01 14:46:56.171 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 296/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.8, lr: 1.022e-03, size: 320, ETA: 1:42:20
2025-09-01 14:46:59.156 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 296/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 3.4, cls_loss: 0.7, lr: 1.021e-03, size: 288, ETA: 1:42:16
2025-09-01 14:47:00.536 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:47:06.706 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:47:07.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:47:08.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6016
2025-09-01 14:47:08.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4999
2025-09-01 14:47:08.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3891
2025-09-01 14:47:08.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4969
2025-09-01 14:47:08.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:47:08.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:47:08.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.602
2025-09-01 14:47:08.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-09-01 14:47:08.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-09-01 14:47:08.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-09-01 14:47:08.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:47:08.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:47:08.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:47:08.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:47:08.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:47:08.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:47:08.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:47:08.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:47:08.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:47:09.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:47:09.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:47:10.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:47:11.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:47:12.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:47:13.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:47:13.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:47:14.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:47:15.546 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:47:15.547 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:47:15.547 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 14:47:15.547 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:47:15.554 | 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: 7.22 ms

2025-09-01 14:47:15.558 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:47:15.639 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:47:15.725 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch297
2025-09-01 14:47:18.568 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 297/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.020e-03, size: 352, ETA: 1:42:12
2025-09-01 14:47:21.599 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 297/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 0.8, lr: 1.019e-03, size: 576, ETA: 1:42:08
2025-09-01 14:47:24.725 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 297/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.8, lr: 1.019e-03, size: 576, ETA: 1:42:05
2025-09-01 14:47:27.925 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 297/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.6, lr: 1.018e-03, size: 416, ETA: 1:42:02
2025-09-01 14:47:30.818 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 297/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 1.017e-03, size: 320, ETA: 1:41:59
2025-09-01 14:47:33.785 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 297/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 1.016e-03, size: 512, ETA: 1:41:55
2025-09-01 14:47:35.124 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:47:41.326 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:47:41.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:47:42.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5781
2025-09-01 14:47:42.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4931
2025-09-01 14:47:42.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3714
2025-09-01 14:47:42.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4809
2025-09-01 14:47:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:47:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:47:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-09-01 14:47:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-09-01 14:47:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-09-01 14:47:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-09-01 14:47:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:47:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:47:42.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:47:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:47:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:47:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:47:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:47:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:47:42.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:47:43.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:47:43.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:47:44.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:47:44.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:47:45.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:47:45.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:47:46.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:47:46.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:47:47.377 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:47:47.377 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:47:47.377 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:47:47.377 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:47:47.384 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.86 ms, Average inference time: 7.06 ms

2025-09-01 14:47:47.385 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:47:47.466 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:47:47.582 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch298
2025-09-01 14:47:50.438 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 298/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 3.9, cls_loss: 0.6, lr: 1.015e-03, size: 416, ETA: 1:41:50
2025-09-01 14:47:53.393 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 298/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.9, lr: 1.014e-03, size: 480, ETA: 1:41:47
2025-09-01 14:47:56.448 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 298/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.9, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 4.6, cls_loss: 0.7, lr: 1.013e-03, size: 448, ETA: 1:41:44
2025-09-01 14:47:59.486 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 298/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 3.3, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.5, lr: 1.012e-03, size: 288, ETA: 1:41:41
2025-09-01 14:48:02.519 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 298/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.8, lr: 1.012e-03, size: 352, ETA: 1:41:37
2025-09-01 14:48:05.546 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 298/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.6, lr: 1.011e-03, size: 320, ETA: 1:41:34
2025-09-01 14:48:06.896 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:48:13.127 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:48:13.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:48:14.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5979
2025-09-01 14:48:14.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5073
2025-09-01 14:48:14.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3911
2025-09-01 14:48:14.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4988
2025-09-01 14:48:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:48:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:48:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-09-01 14:48:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-09-01 14:48:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-09-01 14:48:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.499
2025-09-01 14:48:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:48:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:48:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:48:14.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:48:14.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:48:14.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:48:14.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:48:14.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:48:14.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:48:15.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:48:16.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:48:17.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:48:17.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:48:18.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:48:19.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:48:19.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:48:20.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:48:21.417 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:48:21.417 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:48:21.417 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 14:48:21.417 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:48:21.424 | 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

2025-09-01 14:48:21.426 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:48:21.518 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:48:21.597 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch299
2025-09-01 14:48:24.488 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 299/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 9.4, iou_loss: 3.2, l1_loss: 0.9, conf_loss: 4.4, cls_loss: 0.9, lr: 1.010e-03, size: 576, ETA: 1:41:29
2025-09-01 14:48:27.654 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 299/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.009e-03, size: 480, ETA: 1:41:26
2025-09-01 14:48:30.676 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 299/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.7, lr: 1.008e-03, size: 288, ETA: 1:41:23
2025-09-01 14:48:33.696 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 299/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.006s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.7, lr: 1.007e-03, size: 448, ETA: 1:41:19
2025-09-01 14:48:36.769 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 299/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.6, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 4.1, cls_loss: 0.9, lr: 1.006e-03, size: 544, ETA: 1:41:16
2025-09-01 14:48:39.794 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 299/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 1.006e-03, size: 576, ETA: 1:41:13
2025-09-01 14:48:41.177 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:48:47.380 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:48:48.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:48:48.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5933
2025-09-01 14:48:48.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5037
2025-09-01 14:48:48.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3751
2025-09-01 14:48:48.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4907
2025-09-01 14:48:48.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:48:48.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:48:48.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-09-01 14:48:48.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-09-01 14:48:48.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-09-01 14:48:48.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.491
2025-09-01 14:48:48.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:48:48.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:48:48.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:48:48.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:48:48.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:48:48.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:48:48.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:48:48.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:48:48.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:48:49.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:48:50.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:48:50.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:48:51.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:48:52.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:48:52.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:48:53.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:48:54.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:48:54.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:48:54.866 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:48:54.866 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:48:54.866 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:48:54.873 | 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

2025-09-01 14:48:54.875 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:48:54.959 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:48:55.039 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch300
2025-09-01 14:48:58.156 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 300/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.6, lr: 1.004e-03, size: 480, ETA: 1:41:08
2025-09-01 14:49:01.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 300/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.004e-03, size: 576, ETA: 1:41:05
2025-09-01 14:49:04.463 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 300/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.8, lr: 1.003e-03, size: 256, ETA: 1:41:02
2025-09-01 14:49:07.562 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 300/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, 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: 1.002e-03, size: 480, ETA: 1:40:59
2025-09-01 14:49:10.662 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 300/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 9.6, iou_loss: 3.7, l1_loss: 1.5, conf_loss: 3.3, cls_loss: 1.0, lr: 1.001e-03, size: 352, ETA: 1:40:56
2025-09-01 14:49:13.974 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 300/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.164s, data_time: 0.003s, total_loss: 4.0, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 1.6, cls_loss: 0.5, lr: 1.000e-03, size: 288, ETA: 1:40:53
2025-09-01 14:49:15.348 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:49:21.732 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:49:22.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:49:23.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6030
2025-09-01 14:49:23.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5085
2025-09-01 14:49:23.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3943
2025-09-01 14:49:23.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5019
2025-09-01 14:49:23.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:49:23.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:49:23.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.603
2025-09-01 14:49:23.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.508
2025-09-01 14:49:23.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-09-01 14:49:23.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 14:49:23.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:49:23.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:49:23.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:49:23.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:49:23.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:49:23.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:49:23.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:49:23.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:49:23.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:49:24.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:49:25.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:49:26.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:49:27.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:49:28.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:49:29.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:49:30.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:49:30.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:49:31.906 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:49:31.906 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 14:49:31.907 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 14:49:31.907 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:49:31.914 | 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: 7.13 ms

2025-09-01 14:49:31.916 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:49:32.002 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:49:32.083 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch301
2025-09-01 14:49:34.942 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 301/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 9.992e-04, size: 480, ETA: 1:40:48
2025-09-01 14:49:37.916 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 301/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.8, lr: 9.984e-04, size: 544, ETA: 1:40:44
2025-09-01 14:49:40.982 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 301/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 1.1, lr: 9.976e-04, size: 448, ETA: 1:40:41
2025-09-01 14:49:43.980 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 301/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 9.968e-04, size: 480, ETA: 1:40:38
2025-09-01 14:49:47.121 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 301/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, 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: 9.959e-04, size: 384, ETA: 1:40:35
2025-09-01 14:49:50.199 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 301/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.7, lr: 9.951e-04, size: 448, ETA: 1:40:32
2025-09-01 14:49:51.571 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:49:57.995 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:49:59.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:50:00.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5833
2025-09-01 14:50:00.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5165
2025-09-01 14:50:00.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3642
2025-09-01 14:50:00.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4880
2025-09-01 14:50:00.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:50:00.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:50:00.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.583
2025-09-01 14:50:00.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 14:50:00.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-09-01 14:50:00.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-09-01 14:50:00.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:50:00.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:50:00.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:50:00.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:50:00.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:50:00.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:50:00.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:50:00.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:50:00.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:50:01.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:50:02.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:50:03.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:50:05.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:50:06.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:50:07.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:50:08.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:50:09.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:50:10.587 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:50:10.587 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:50:10.587 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:50:10.588 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:50:10.595 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.97 ms, Average inference time: 7.25 ms

2025-09-01 14:50:10.596 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:50:10.713 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:50:10.790 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch302
2025-09-01 14:50:13.718 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 302/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 9.940e-04, size: 416, ETA: 1:40:27
2025-09-01 14:50:16.708 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 302/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, 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: 9.931e-04, size: 512, ETA: 1:40:23
2025-09-01 14:50:19.716 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 302/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 9.923e-04, size: 448, ETA: 1:40:20
2025-09-01 14:50:22.823 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 302/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 9.915e-04, size: 576, ETA: 1:40:17
2025-09-01 14:50:25.967 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 302/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.6, lr: 9.907e-04, size: 512, ETA: 1:40:14
2025-09-01 14:50:29.100 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 302/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.8, lr: 9.899e-04, size: 448, ETA: 1:40:11
2025-09-01 14:50:30.554 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:50:37.010 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:50:38.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:50:39.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5589
2025-09-01 14:50:39.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5221
2025-09-01 14:50:39.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3528
2025-09-01 14:50:39.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4780
2025-09-01 14:50:39.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:50:39.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:50:39.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.559
2025-09-01 14:50:39.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-09-01 14:50:39.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.353
2025-09-01 14:50:39.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-09-01 14:50:39.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:50:39.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:50:39.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:50:39.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:50:39.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:50:39.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:50:39.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:50:39.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:50:39.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:50:40.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:50:42.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:50:43.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:50:44.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:50:45.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:50:47.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:50:48.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:50:49.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:50:51.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:50:51.010 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:50:51.011 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:50:51.011 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:50:51.019 | 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

2025-09-01 14:50:51.020 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:50:51.187 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:50:51.263 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch303
2025-09-01 14:50:54.062 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 303/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 9.887e-04, size: 416, ETA: 1:40:06
2025-09-01 14:50:57.120 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 303/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, 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: 9.879e-04, size: 384, ETA: 1:40:03
2025-09-01 14:51:00.008 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 303/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 9.4, iou_loss: 3.5, l1_loss: 1.5, conf_loss: 3.7, cls_loss: 0.7, lr: 9.871e-04, size: 256, ETA: 1:39:59
2025-09-01 14:51:03.098 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 303/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.8, lr: 9.863e-04, size: 256, ETA: 1:39:56
2025-09-01 14:51:06.232 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 303/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.7, lr: 9.855e-04, size: 384, ETA: 1:39:53
2025-09-01 14:51:09.347 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 303/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 9.847e-04, size: 352, ETA: 1:39:50
2025-09-01 14:51:10.685 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:51:17.010 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:51:18.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:51:18.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5859
2025-09-01 14:51:18.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5167
2025-09-01 14:51:18.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3514
2025-09-01 14:51:18.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4847
2025-09-01 14:51:18.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:51:18.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:51:18.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-09-01 14:51:18.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 14:51:18.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.351
2025-09-01 14:51:18.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.485
2025-09-01 14:51:18.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:51:18.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:51:18.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:51:18.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:51:18.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:51:18.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:51:18.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:51:18.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:51:18.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:51:19.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:51:20.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:51:21.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:51:22.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:51:23.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:51:23.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:51:24.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:51:25.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:51:26.560 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:51:26.561 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:51:26.561 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:51:26.561 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:51:26.569 | 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: 7.26 ms

2025-09-01 14:51:26.569 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:51:26.656 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:51:26.791 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch304
2025-09-01 14:51:29.598 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 304/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.8, lr: 9.835e-04, size: 544, ETA: 1:39:45
2025-09-01 14:51:32.697 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 304/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, 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: 9.827e-04, size: 288, ETA: 1:39:41
2025-09-01 14:51:35.613 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 304/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.7, lr: 9.819e-04, size: 288, ETA: 1:39:38
2025-09-01 14:51:38.609 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 304/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 9.810e-04, size: 448, ETA: 1:39:35
2025-09-01 14:51:41.631 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 304/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 9.802e-04, size: 352, ETA: 1:39:32
2025-09-01 14:51:44.660 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 304/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.9, lr: 9.794e-04, size: 544, ETA: 1:39:28
2025-09-01 14:51:46.056 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:51:52.494 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:51:53.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:51:53.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5008
2025-09-01 14:51:53.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4795
2025-09-01 14:51:54.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3041
2025-09-01 14:51:54.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4281
2025-09-01 14:51:54.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:51:54.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:51:54.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-09-01 14:51:54.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-09-01 14:51:54.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.304
2025-09-01 14:51:54.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.428
2025-09-01 14:51:54.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:51:54.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:51:54.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:51:54.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:51:54.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:51:54.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:51:54.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:51:54.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:51:54.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:51:54.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:51:55.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:51:56.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:51:56.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:51:57.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:51:58.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:51:58.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:51:59.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:51:59.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:51:59.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 14:51:59.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.43
2025-09-01 14:51:59.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:51:59.960 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.89 ms, Average inference time: 7.08 ms

2025-09-01 14:51:59.967 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:52:00.054 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:52:00.136 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch305
2025-09-01 14:52:03.087 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 305/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.6, lr: 9.782e-04, size: 448, ETA: 1:39:23
2025-09-01 14:52:06.129 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 305/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 9.774e-04, size: 384, ETA: 1:39:20
2025-09-01 14:52:09.121 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 305/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 9.766e-04, size: 384, ETA: 1:39:17
2025-09-01 14:52:12.318 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 305/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.158s, 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: 9.758e-04, size: 320, ETA: 1:39:14
2025-09-01 14:52:15.406 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 305/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 9.4, iou_loss: 2.6, l1_loss: 1.6, conf_loss: 4.5, cls_loss: 0.7, lr: 9.750e-04, size: 576, ETA: 1:39:11
2025-09-01 14:52:18.538 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 305/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.7, cls_loss: 0.6, lr: 9.742e-04, size: 416, ETA: 1:39:07
2025-09-01 14:52:19.854 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:52:26.176 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:52:26.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:52:27.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5760
2025-09-01 14:52:27.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4863
2025-09-01 14:52:27.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3623
2025-09-01 14:52:27.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4748
2025-09-01 14:52:27.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:52:27.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:52:27.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-09-01 14:52:27.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.486
2025-09-01 14:52:27.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-09-01 14:52:27.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.475
2025-09-01 14:52:27.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:52:27.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:52:27.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:52:27.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:52:27.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:52:27.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:52:27.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:52:27.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:52:27.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:52:28.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:52:28.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:52:29.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:52:30.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:52:30.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:52:31.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:52:32.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:52:32.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:52:33.571 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:52:33.572 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:52:33.572 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 14:52:33.572 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:52:33.579 | 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: 7.10 ms

2025-09-01 14:52:33.582 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:52:33.668 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:52:33.749 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch306
2025-09-01 14:52:36.593 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 306/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 9.730e-04, size: 352, ETA: 1:39:03
2025-09-01 14:52:39.613 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 306/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.8, lr: 9.722e-04, size: 256, ETA: 1:38:59
2025-09-01 14:52:42.671 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 306/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 9.714e-04, size: 512, ETA: 1:38:56
2025-09-01 14:52:45.810 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 306/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 9.706e-04, size: 576, ETA: 1:38:53
2025-09-01 14:52:48.968 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 306/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 1.0, lr: 9.698e-04, size: 416, ETA: 1:38:50
2025-09-01 14:52:51.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 306/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.2, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.8, lr: 9.690e-04, size: 256, ETA: 1:38:47
2025-09-01 14:52:53.239 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:52:59.419 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:53:00.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:53:01.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5734
2025-09-01 14:53:01.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5294
2025-09-01 14:53:01.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3775
2025-09-01 14:53:01.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4934
2025-09-01 14:53:01.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:53:01.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:53:01.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.573
2025-09-01 14:53:01.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-09-01 14:53:01.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-09-01 14:53:01.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-09-01 14:53:01.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:53:01.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:53:01.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:53:01.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:53:01.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:53:01.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:53:01.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:53:01.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:53:01.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:53:02.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:53:03.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:53:04.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:53:05.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:53:06.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:53:07.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:53:08.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:53:09.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:53:10.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:53:10.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:53:10.085 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:53:10.085 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:53:10.092 | 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: 7.11 ms

2025-09-01 14:53:10.099 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:53:10.257 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:53:10.332 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch307
2025-09-01 14:53:13.185 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 307/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 9.678e-04, size: 448, ETA: 1:38:42
2025-09-01 14:53:16.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 307/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.9, lr: 9.670e-04, size: 416, ETA: 1:38:38
2025-09-01 14:53:19.237 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 307/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 9.662e-04, size: 448, ETA: 1:38:35
2025-09-01 14:53:22.403 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 307/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.0, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.6, lr: 9.653e-04, size: 448, ETA: 1:38:32
2025-09-01 14:53:25.502 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 307/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 9.645e-04, size: 512, ETA: 1:38:29
2025-09-01 14:53:28.555 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 307/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 9.637e-04, size: 416, ETA: 1:38:25
2025-09-01 14:53:29.862 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:53:36.013 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:53:37.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:53:37.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5951
2025-09-01 14:53:38.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5251
2025-09-01 14:53:38.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3563
2025-09-01 14:53:38.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4922
2025-09-01 14:53:38.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:53:38.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:53:38.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.595
2025-09-01 14:53:38.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-09-01 14:53:38.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-09-01 14:53:38.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-09-01 14:53:38.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:53:38.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:53:38.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:53:38.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:53:38.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:53:38.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:53:38.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:53:38.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:53:38.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:53:39.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:53:40.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:53:41.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:53:42.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:53:43.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:53:44.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:53:45.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:53:45.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:53:46.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:53:46.979 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 14:53:46.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:53:46.980 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:53:46.987 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.34 ms, Average NMS time: 0.98 ms, Average inference time: 7.31 ms

2025-09-01 14:53:47.001 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:53:47.084 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:53:47.167 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch308
2025-09-01 14:53:50.107 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 308/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 1.0, lr: 9.625e-04, size: 256, ETA: 1:38:21
2025-09-01 14:53:53.167 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 308/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 9.617e-04, size: 576, ETA: 1:38:17
2025-09-01 14:53:56.244 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 308/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 9.609e-04, size: 256, ETA: 1:38:14
2025-09-01 14:53:59.322 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 308/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 9.601e-04, size: 288, ETA: 1:38:11
2025-09-01 14:54:02.373 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 308/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 9.593e-04, size: 320, ETA: 1:38:08
2025-09-01 14:54:05.417 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 308/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 9.585e-04, size: 576, ETA: 1:38:05
2025-09-01 14:54:06.878 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:54:13.200 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:54:13.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:54:14.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5822
2025-09-01 14:54:14.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4989
2025-09-01 14:54:14.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3363
2025-09-01 14:54:14.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4725
2025-09-01 14:54:14.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:54:14.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:54:14.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-09-01 14:54:14.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-09-01 14:54:14.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.336
2025-09-01 14:54:14.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.472
2025-09-01 14:54:14.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:54:14.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:54:14.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:54:14.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:54:14.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:54:14.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:54:14.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:54:14.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:54:14.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:54:14.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:54:15.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:54:16.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:54:16.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:54:17.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:54:17.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:54:18.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:54:18.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:54:19.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:54:19.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:54:19.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 14:54:19.429 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:54:19.439 | 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

2025-09-01 14:54:19.440 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:54:19.523 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:54:19.612 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch309
2025-09-01 14:54:22.541 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 309/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.3, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 6.3, cls_loss: 0.0, lr: 9.573e-04, size: 384, ETA: 1:38:00
2025-09-01 14:54:25.530 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 309/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.7, lr: 9.565e-04, size: 544, ETA: 1:37:56
2025-09-01 14:54:28.612 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 309/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.8, lr: 9.557e-04, size: 288, ETA: 1:37:53
2025-09-01 14:54:31.728 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 309/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.7, lr: 9.549e-04, size: 576, ETA: 1:37:50
2025-09-01 14:54:34.803 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 309/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, 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: 9.541e-04, size: 512, ETA: 1:37:47
2025-09-01 14:54:37.802 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 309/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 9.533e-04, size: 384, ETA: 1:37:44
2025-09-01 14:54:39.173 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:54:45.588 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:54:46.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:54:46.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5667
2025-09-01 14:54:46.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5009
2025-09-01 14:54:46.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3658
2025-09-01 14:54:46.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4778
2025-09-01 14:54:46.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:54:46.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:54:46.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.567
2025-09-01 14:54:46.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-09-01 14:54:46.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-09-01 14:54:46.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-09-01 14:54:46.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:54:46.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:54:46.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:54:46.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:54:46.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:54:46.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:54:46.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:54:46.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:54:46.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:54:47.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:54:48.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:54:48.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:54:49.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:54:49.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:54:50.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:54:51.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:54:51.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:54:52.218 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:54:52.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 14:54:52.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:54:52.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:54:52.226 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.90 ms, Average inference time: 7.19 ms

2025-09-01 14:54:52.227 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:54:52.352 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:54:52.468 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch310
2025-09-01 14:54:55.429 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 310/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, 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: 9.521e-04, size: 384, ETA: 1:37:39
2025-09-01 14:54:58.616 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 310/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 9.513e-04, size: 384, ETA: 1:37:36
2025-09-01 14:55:01.610 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 310/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 9.505e-04, size: 512, ETA: 1:37:32
2025-09-01 14:55:04.686 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 310/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, 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: 9.497e-04, size: 320, ETA: 1:37:29
2025-09-01 14:55:07.846 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 310/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, 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: 9.488e-04, size: 352, ETA: 1:37:26
2025-09-01 14:55:10.804 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 310/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 9.480e-04, size: 352, ETA: 1:37:23
2025-09-01 14:55:12.110 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:55:18.414 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:55:19.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:55:19.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5990
2025-09-01 14:55:20.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4997
2025-09-01 14:55:20.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3706
2025-09-01 14:55:20.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4898
2025-09-01 14:55:20.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:55:20.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:55:20.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-09-01 14:55:20.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-09-01 14:55:20.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-09-01 14:55:20.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-09-01 14:55:20.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:55:20.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:55:20.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:55:20.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:55:20.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:55:20.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:55:20.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:55:20.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:55:20.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:55:20.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:55:21.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:55:22.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:55:23.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:55:23.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:55:24.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:55:25.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:55:26.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:55:26.806 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:55:26.807 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:55:26.807 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:55:26.807 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:55:26.814 | 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

2025-09-01 14:55:26.815 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:55:26.940 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:55:27.025 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch311
2025-09-01 14:55:30.067 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 311/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.8, lr: 9.469e-04, size: 544, ETA: 1:37:18
2025-09-01 14:55:33.084 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 311/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 9.460e-04, size: 512, ETA: 1:37:15
2025-09-01 14:55:36.053 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 311/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.8, lr: 9.452e-04, size: 416, ETA: 1:37:12
2025-09-01 14:55:39.045 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 311/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 9.4, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 4.6, cls_loss: 0.8, lr: 9.444e-04, size: 480, ETA: 1:37:08
2025-09-01 14:55:42.007 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 311/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 9.436e-04, size: 256, ETA: 1:37:05
2025-09-01 14:55:45.058 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 311/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.9, lr: 9.428e-04, size: 320, ETA: 1:37:02
2025-09-01 14:55:46.360 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:55:52.590 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:55:53.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:55:55.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5926
2025-09-01 14:55:55.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5055
2025-09-01 14:55:55.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3724
2025-09-01 14:55:55.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4901
2025-09-01 14:55:55.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:55:55.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:55:55.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-09-01 14:55:55.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-09-01 14:55:55.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-09-01 14:55:55.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-09-01 14:55:55.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:55:55.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:55:55.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:55:55.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:55:55.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:55:55.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:55:55.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:55:55.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:55:55.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:55:56.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:55:57.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:55:58.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:56:00.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:56:01.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:56:02.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:56:03.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:56:04.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:56:06.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:56:06.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:56:06.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:56:06.197 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:56:06.205 | 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

2025-09-01 14:56:06.206 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:56:06.337 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:56:06.412 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch312
2025-09-01 14:56:09.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 312/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 9.416e-04, size: 416, ETA: 1:36:57
2025-09-01 14:56:12.345 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 312/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 9.408e-04, size: 256, ETA: 1:36:54
2025-09-01 14:56:15.440 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 312/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 9.400e-04, size: 544, ETA: 1:36:50
2025-09-01 14:56:18.488 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 312/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, 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: 9.392e-04, size: 512, ETA: 1:36:47
2025-09-01 14:56:21.517 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 312/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 9.384e-04, size: 384, ETA: 1:36:44
2025-09-01 14:56:24.518 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 312/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 8.2, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 3.1, cls_loss: 0.9, lr: 9.376e-04, size: 512, ETA: 1:36:41
2025-09-01 14:56:25.928 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:56:32.209 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:56:33.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:56:33.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5883
2025-09-01 14:56:33.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5141
2025-09-01 14:56:34.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3972
2025-09-01 14:56:34.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4999
2025-09-01 14:56:34.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:56:34.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:56:34.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.588
2025-09-01 14:56:34.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.514
2025-09-01 14:56:34.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-09-01 14:56:34.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-09-01 14:56:34.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:56:34.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:56:34.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:56:34.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:56:34.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:56:34.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:56:34.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:56:34.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:56:34.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:56:34.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:56:35.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:56:36.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:56:37.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:56:38.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:56:39.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:56:39.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:56:40.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:56:41.420 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:56:41.420 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 14:56:41.420 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 14:56:41.420 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:56:41.428 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.42 ms, Average NMS time: 0.94 ms, Average inference time: 7.35 ms

2025-09-01 14:56:41.434 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:56:41.571 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:56:41.645 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch313
2025-09-01 14:56:44.648 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 313/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.8, lr: 9.364e-04, size: 512, ETA: 1:36:36
2025-09-01 14:56:47.629 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 313/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 1.0, lr: 9.356e-04, size: 288, ETA: 1:36:33
2025-09-01 14:56:50.764 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 313/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, 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: 9.348e-04, size: 576, ETA: 1:36:30
2025-09-01 14:56:54.043 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 313/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 9.340e-04, size: 448, ETA: 1:36:27
2025-09-01 14:56:57.112 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 313/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 3.1, cls_loss: 0.8, lr: 9.332e-04, size: 288, ETA: 1:36:23
2025-09-01 14:57:00.128 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 313/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.8, conf_loss: 2.6, cls_loss: 0.7, lr: 9.323e-04, size: 576, ETA: 1:36:20
2025-09-01 14:57:01.501 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:57:07.665 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:57:08.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:57:09.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5813
2025-09-01 14:57:09.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4994
2025-09-01 14:57:09.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3629
2025-09-01 14:57:09.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4812
2025-09-01 14:57:09.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:57:09.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:57:09.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.581
2025-09-01 14:57:09.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-09-01 14:57:09.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-09-01 14:57:09.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-09-01 14:57:09.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:57:09.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:57:09.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:57:09.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:57:09.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:57:09.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:57:09.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:57:09.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:57:09.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:57:10.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:57:11.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:57:12.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:57:13.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:57:13.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:57:14.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:57:15.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:57:16.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:57:17.463 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:57:17.463 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:57:17.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 14:57:17.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:57:17.472 | 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

2025-09-01 14:57:17.473 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:57:17.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:57:17.643 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch314
2025-09-01 14:57:20.512 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 314/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.7, lr: 9.312e-04, size: 256, ETA: 1:36:15
2025-09-01 14:57:23.524 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 314/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.8, lr: 9.304e-04, size: 576, ETA: 1:36:12
2025-09-01 14:57:26.594 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 314/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.8, lr: 9.296e-04, size: 512, ETA: 1:36:09
2025-09-01 14:57:29.733 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 314/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.006s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.9, lr: 9.287e-04, size: 352, ETA: 1:36:06
2025-09-01 14:57:32.700 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 314/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.9, lr: 9.279e-04, size: 320, ETA: 1:36:02
2025-09-01 14:57:35.673 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 314/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 3.9, cls_loss: 0.9, lr: 9.271e-04, size: 544, ETA: 1:35:59
2025-09-01 14:57:37.045 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:57:43.196 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:57:44.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:57:44.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6002
2025-09-01 14:57:44.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5195
2025-09-01 14:57:44.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3660
2025-09-01 14:57:44.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4952
2025-09-01 14:57:44.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:57:44.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:57:44.937 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-09-01 14:57:44.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-09-01 14:57:44.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-09-01 14:57:44.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-09-01 14:57:44.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:57:44.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:57:44.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:57:44.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:57:44.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:57:44.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:57:44.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:57:44.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:57:44.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:57:45.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:57:46.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:57:47.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:57:48.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:57:48.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:57:49.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:57:50.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:57:51.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:57:52.063 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:57:52.063 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:57:52.063 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 14:57:52.063 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:57:52.070 | 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

2025-09-01 14:57:52.071 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:57:52.158 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:57:52.242 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch315
2025-09-01 14:57:55.173 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 315/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, 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: 9.260e-04, size: 288, ETA: 1:35:54
2025-09-01 14:57:58.284 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 315/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 9.251e-04, size: 416, ETA: 1:35:51
2025-09-01 14:58:01.293 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 315/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.8, lr: 9.243e-04, size: 544, ETA: 1:35:48
2025-09-01 14:58:04.309 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 315/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.8, lr: 9.235e-04, size: 448, ETA: 1:35:45
2025-09-01 14:58:07.309 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 315/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.6, lr: 9.227e-04, size: 512, ETA: 1:35:41
2025-09-01 14:58:10.380 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 315/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.7, lr: 9.219e-04, size: 352, ETA: 1:35:38
2025-09-01 14:58:11.783 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:58:17.950 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:58:18.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:58:18.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5770
2025-09-01 14:58:19.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5149
2025-09-01 14:58:19.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3264
2025-09-01 14:58:19.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4728
2025-09-01 14:58:19.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:58:19.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:58:19.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.577
2025-09-01 14:58:19.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 14:58:19.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.326
2025-09-01 14:58:19.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.473
2025-09-01 14:58:19.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:58:19.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:58:19.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:58:19.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:58:19.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:58:19.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:58:19.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:58:19.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:58:19.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:58:19.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:58:20.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:58:20.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:58:21.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:58:21.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:58:22.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:58:22.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:58:23.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:58:23.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:58:23.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 14:58:23.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 14:58:23.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:58:23.903 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.87 ms, Average inference time: 7.12 ms

2025-09-01 14:58:23.905 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:58:23.993 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:58:24.072 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch316
2025-09-01 14:58:26.898 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 316/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 9.207e-04, size: 448, ETA: 1:35:33
2025-09-01 14:58:30.090 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 316/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.159s, 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: 9.199e-04, size: 320, ETA: 1:35:30
2025-09-01 14:58:32.964 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 316/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, 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: 9.191e-04, size: 320, ETA: 1:35:27
2025-09-01 14:58:36.073 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 316/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 9.183e-04, size: 256, ETA: 1:35:24
2025-09-01 14:58:39.155 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 316/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, 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: 9.175e-04, size: 256, ETA: 1:35:20
2025-09-01 14:58:42.260 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 316/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.4, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 9.167e-04, size: 448, ETA: 1:35:17
2025-09-01 14:58:43.656 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:58:49.871 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:58:50.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:58:51.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5963
2025-09-01 14:58:51.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5151
2025-09-01 14:58:51.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3680
2025-09-01 14:58:51.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4931
2025-09-01 14:58:51.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:58:51.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:58:51.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.596
2025-09-01 14:58:51.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 14:58:51.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-09-01 14:58:51.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-09-01 14:58:51.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:58:51.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:58:51.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:58:51.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:58:51.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:58:51.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:58:51.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:58:51.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:58:51.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:58:52.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:58:53.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:58:53.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:58:54.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:58:55.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:58:56.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:58:57.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:58:57.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:58:58.608 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:58:58.609 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 14:58:58.609 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 14:58:58.609 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:58:58.616 | 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

2025-09-01 14:58:58.622 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:58:58.708 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:58:58.833 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch317
2025-09-01 14:59:01.698 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 317/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 9.155e-04, size: 416, ETA: 1:35:12
2025-09-01 14:59:04.691 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 317/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 7.2, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 9.147e-04, size: 416, ETA: 1:35:09
2025-09-01 14:59:07.633 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 317/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.3, iou_loss: 1.8, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 9.139e-04, size: 512, ETA: 1:35:06
2025-09-01 14:59:10.671 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 317/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 9.131e-04, size: 320, ETA: 1:35:03
2025-09-01 14:59:13.622 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 317/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 3.3, cls_loss: 0.8, lr: 9.123e-04, size: 352, ETA: 1:34:59
2025-09-01 14:59:16.651 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 317/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 9.115e-04, size: 352, ETA: 1:34:56
2025-09-01 14:59:18.022 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:59:24.237 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:59:24.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:59:24.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.4993
2025-09-01 14:59:24.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4423
2025-09-01 14:59:25.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2846
2025-09-01 14:59:25.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4087
2025-09-01 14:59:25.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:59:25.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:59:25.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-09-01 14:59:25.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.442
2025-09-01 14:59:25.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.285
2025-09-01 14:59:25.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.409
2025-09-01 14:59:25.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:59:25.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:59:25.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:59:25.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:59:25.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:59:25.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:59:25.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:59:25.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:59:25.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:59:25.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:59:25.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:59:26.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:59:26.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 14:59:26.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 14:59:27.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 14:59:27.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 14:59:27.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 14:59:27.992 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 14:59:27.993 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.21
2025-09-01 14:59:27.993 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.41
2025-09-01 14:59:27.993 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 14:59:27.999 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.27 ms, Average NMS time: 0.81 ms, Average inference time: 7.09 ms

2025-09-01 14:59:28.000 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:59:28.087 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:59:28.167 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch318
2025-09-01 14:59:31.055 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 318/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.8, lr: 9.103e-04, size: 416, ETA: 1:34:51
2025-09-01 14:59:34.070 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 318/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.6, lr: 9.095e-04, size: 288, ETA: 1:34:48
2025-09-01 14:59:37.127 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 318/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.7, lr: 9.087e-04, size: 448, ETA: 1:34:45
2025-09-01 14:59:40.203 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 318/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.8, lr: 9.079e-04, size: 352, ETA: 1:34:42
2025-09-01 14:59:43.341 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 318/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, 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: 9.071e-04, size: 480, ETA: 1:34:38
2025-09-01 14:59:46.356 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 318/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.7, lr: 9.063e-04, size: 352, ETA: 1:34:35
2025-09-01 14:59:47.741 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 14:59:54.015 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 14:59:54.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 14:59:55.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5969
2025-09-01 14:59:55.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5198
2025-09-01 14:59:55.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3599
2025-09-01 14:59:55.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4922
2025-09-01 14:59:55.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 14:59:55.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 14:59:55.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.597
2025-09-01 14:59:55.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-09-01 14:59:55.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.360
2025-09-01 14:59:55.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-09-01 14:59:55.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 14:59:55.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 14:59:55.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 14:59:55.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 14:59:55.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 14:59:55.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 14:59:55.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 14:59:55.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 14:59:55.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 14:59:56.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 14:59:57.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 14:59:58.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 14:59:59.309 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:00:00.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:00:00.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:00:01.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:00:02.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:00:03.534 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:00:03.534 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:00:03.535 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:00:03.535 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:00:03.542 | 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

2025-09-01 15:00:03.544 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:00:03.629 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:00:03.711 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch319
2025-09-01 15:00:06.547 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 319/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.5, lr: 9.051e-04, size: 256, ETA: 1:34:30
2025-09-01 15:00:09.560 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 319/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 9.043e-04, size: 256, ETA: 1:34:27
2025-09-01 15:00:12.474 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 319/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.143s, 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: 9.035e-04, size: 544, ETA: 1:34:24
2025-09-01 15:00:15.520 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 319/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 9.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 5.0, cls_loss: 0.9, lr: 9.027e-04, size: 352, ETA: 1:34:20
2025-09-01 15:00:18.541 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 319/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, 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: 9.019e-04, size: 352, ETA: 1:34:17
2025-09-01 15:00:21.627 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 319/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.8, lr: 9.010e-04, size: 288, ETA: 1:34:14
2025-09-01 15:00:22.993 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:00:29.319 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:00:30.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:00:30.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5897
2025-09-01 15:00:30.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4853
2025-09-01 15:00:30.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3479
2025-09-01 15:00:30.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4743
2025-09-01 15:00:30.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:00:30.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:00:30.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.590
2025-09-01 15:00:30.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-09-01 15:00:30.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.348
2025-09-01 15:00:30.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.474
2025-09-01 15:00:30.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:00:30.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:00:30.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:00:30.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:00:30.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:00:30.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:00:30.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:00:30.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:00:30.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:00:31.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:00:32.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:00:32.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:00:33.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:00:33.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:00:34.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:00:35.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:00:35.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:00:36.552 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:00:36.553 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 15:00:36.553 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 15:00:36.553 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:00:36.560 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.90 ms, Average inference time: 7.19 ms

2025-09-01 15:00:36.561 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:00:36.651 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:00:36.731 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch320
2025-09-01 15:00:39.630 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 320/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 8.999e-04, size: 256, ETA: 1:34:09
2025-09-01 15:00:42.682 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 320/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 3.3, cls_loss: 0.7, lr: 8.991e-04, size: 512, ETA: 1:34:06
2025-09-01 15:00:45.732 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 320/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.6, lr: 8.983e-04, size: 256, ETA: 1:34:03
2025-09-01 15:00:48.718 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 320/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, 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: 8.974e-04, size: 448, ETA: 1:34:00
2025-09-01 15:00:51.693 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 320/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, 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: 8.966e-04, size: 384, ETA: 1:33:56
2025-09-01 15:00:54.672 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 320/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, 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: 8.958e-04, size: 384, ETA: 1:33:53
2025-09-01 15:00:56.039 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:01:02.398 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:01:03.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:01:04.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5976
2025-09-01 15:01:04.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5076
2025-09-01 15:01:04.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3758
2025-09-01 15:01:04.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4937
2025-09-01 15:01:04.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:01:04.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:01:04.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-09-01 15:01:04.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.508
2025-09-01 15:01:04.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-09-01 15:01:04.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-09-01 15:01:04.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:01:04.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:01:04.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:01:04.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:01:04.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:01:04.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:01:04.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:01:04.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:01:04.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:01:05.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:01:06.151 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:01:07.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:01:07.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:01:08.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:01:09.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:01:10.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:01:11.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:01:12.394 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:01:12.394 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:01:12.394 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:01:12.394 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:01:12.402 | 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: 7.15 ms

2025-09-01 15:01:12.403 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:01:12.485 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:01:12.580 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch321
2025-09-01 15:01:15.382 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 321/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 4.2, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.6, lr: 8.947e-04, size: 416, ETA: 1:33:48
2025-09-01 15:01:18.316 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 321/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.5, lr: 8.939e-04, size: 288, ETA: 1:33:45
2025-09-01 15:01:21.276 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 321/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 8.930e-04, size: 416, ETA: 1:33:41
2025-09-01 15:01:24.353 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 321/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 8.922e-04, size: 416, ETA: 1:33:38
2025-09-01 15:01:27.301 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 321/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 8.914e-04, size: 288, ETA: 1:33:35
2025-09-01 15:01:30.271 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 321/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, 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: 8.906e-04, size: 256, ETA: 1:33:32
2025-09-01 15:01:31.634 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:01:37.798 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:01:38.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:01:38.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5694
2025-09-01 15:01:38.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4967
2025-09-01 15:01:38.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3491
2025-09-01 15:01:38.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4717
2025-09-01 15:01:38.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:01:38.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:01:38.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.569
2025-09-01 15:01:38.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-09-01 15:01:38.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.349
2025-09-01 15:01:38.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.472
2025-09-01 15:01:38.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:01:38.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:01:38.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:01:38.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:01:38.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:01:38.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:01:38.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:01:38.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:01:38.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:01:39.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:01:39.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:01:40.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:01:40.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:01:41.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:01:41.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:01:42.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:01:42.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:01:43.075 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:01:43.075 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:01:43.075 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 15:01:43.076 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:01:43.089 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.86 ms, Average inference time: 6.99 ms

2025-09-01 15:01:43.090 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:01:43.209 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:01:43.318 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch322
2025-09-01 15:01:46.235 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 322/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.8, lr: 8.895e-04, size: 512, ETA: 1:33:27
2025-09-01 15:01:49.197 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 322/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, 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: 8.887e-04, size: 320, ETA: 1:33:24
2025-09-01 15:01:52.278 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 322/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.6, lr: 8.878e-04, size: 352, ETA: 1:33:20
2025-09-01 15:01:55.326 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 322/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, 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: 8.870e-04, size: 448, ETA: 1:33:17
2025-09-01 15:01:58.307 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 322/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 8.862e-04, size: 352, ETA: 1:33:14
2025-09-01 15:02:01.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 322/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 8.854e-04, size: 384, ETA: 1:33:11
2025-09-01 15:02:02.676 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:02:08.905 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:02:10.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:02:11.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6025
2025-09-01 15:02:11.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5085
2025-09-01 15:02:11.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3720
2025-09-01 15:02:11.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4943
2025-09-01 15:02:11.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:02:11.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:02:11.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.602
2025-09-01 15:02:11.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-09-01 15:02:11.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-09-01 15:02:11.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-09-01 15:02:11.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:02:11.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:02:11.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:02:11.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:02:11.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:02:11.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:02:11.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:02:11.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:02:11.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:02:12.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:02:13.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:02:15.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:02:16.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:02:17.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:02:18.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:02:19.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:02:21.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:02:22.405 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:02:22.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:02:22.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:02:22.406 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:02:22.414 | 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: 7.10 ms

2025-09-01 15:02:22.415 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:02:22.497 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:02:22.581 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch323
2025-09-01 15:02:25.503 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 323/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.4, lr: 8.843e-04, size: 320, ETA: 1:33:06
2025-09-01 15:02:28.555 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 323/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.7, lr: 8.835e-04, size: 448, ETA: 1:33:03
2025-09-01 15:02:31.636 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 323/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.6, lr: 8.826e-04, size: 512, ETA: 1:33:00
2025-09-01 15:02:34.734 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 323/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.5, lr: 8.818e-04, size: 448, ETA: 1:32:56
2025-09-01 15:02:37.728 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 323/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.6, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 4.2, cls_loss: 0.7, lr: 8.810e-04, size: 448, ETA: 1:32:53
2025-09-01 15:02:40.680 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 323/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, 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: 8.802e-04, size: 384, ETA: 1:32:50
2025-09-01 15:02:42.106 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:02:48.477 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:02:49.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:02:50.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5985
2025-09-01 15:02:50.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5166
2025-09-01 15:02:50.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3902
2025-09-01 15:02:50.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5018
2025-09-01 15:02:50.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:02:50.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:02:50.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-09-01 15:02:50.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 15:02:50.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-09-01 15:02:50.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 15:02:50.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:02:50.203 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:02:50.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:02:50.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:02:50.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:02:50.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:02:50.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:02:50.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:02:50.204 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:02:51.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:02:51.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:02:52.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:02:53.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:02:54.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:02:55.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:02:55.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:02:56.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:02:57.518 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:02:57.518 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:02:57.518 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:02:57.518 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:02:57.532 | 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: 7.09 ms

2025-09-01 15:02:57.533 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:02:57.658 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:02:57.764 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch324
2025-09-01 15:03:00.645 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 324/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, 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: 8.791e-04, size: 512, ETA: 1:32:45
2025-09-01 15:03:03.651 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 324/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.8, lr: 8.783e-04, size: 320, ETA: 1:32:42
2025-09-01 15:03:06.612 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 324/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, 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: 8.774e-04, size: 384, ETA: 1:32:39
2025-09-01 15:03:09.685 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 324/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 1.0, lr: 8.766e-04, size: 416, ETA: 1:32:35
2025-09-01 15:03:12.673 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 324/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.5, conf_loss: 1.9, cls_loss: 0.6, lr: 8.758e-04, size: 480, ETA: 1:32:32
2025-09-01 15:03:15.658 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 324/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 8.750e-04, size: 544, ETA: 1:32:29
2025-09-01 15:03:17.072 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:03:23.374 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:03:24.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:03:24.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6039
2025-09-01 15:03:25.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5204
2025-09-01 15:03:25.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3749
2025-09-01 15:03:25.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4998
2025-09-01 15:03:25.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:03:25.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:03:25.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-09-01 15:03:25.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-09-01 15:03:25.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-09-01 15:03:25.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-09-01 15:03:25.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:03:25.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:03:25.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:03:25.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:03:25.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:03:25.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:03:25.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:03:25.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:03:25.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:03:25.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:03:26.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:03:27.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:03:28.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:03:29.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:03:29.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:03:30.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:03:31.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:03:32.190 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:03:32.191 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:03:32.191 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:03:32.192 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:03:32.204 | 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

2025-09-01 15:03:32.205 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:03:32.377 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:03:32.508 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch325
2025-09-01 15:03:35.392 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 325/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 4.1, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.6, lr: 8.739e-04, size: 320, ETA: 1:32:24
2025-09-01 15:03:38.485 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 325/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 8.731e-04, size: 512, ETA: 1:32:21
2025-09-01 15:03:41.568 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 325/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, 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: 8.723e-04, size: 448, ETA: 1:32:18
2025-09-01 15:03:44.587 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 325/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 8.714e-04, size: 544, ETA: 1:32:14
2025-09-01 15:03:47.684 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 325/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 8.706e-04, size: 480, ETA: 1:32:11
2025-09-01 15:03:50.710 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 325/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 8.698e-04, size: 512, ETA: 1:32:08
2025-09-01 15:03:52.088 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:03:58.178 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:03:59.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:03:59.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5796
2025-09-01 15:03:59.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5163
2025-09-01 15:03:59.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3212
2025-09-01 15:03:59.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4723
2025-09-01 15:03:59.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:03:59.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:03:59.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.580
2025-09-01 15:03:59.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-09-01 15:03:59.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.321
2025-09-01 15:03:59.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.472
2025-09-01 15:03:59.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:03:59.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:03:59.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:03:59.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:03:59.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:03:59.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:03:59.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:03:59.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:03:59.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:04:00.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:04:01.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:04:01.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:04:02.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:04:03.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:04:03.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:04:04.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:04:05.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:04:05.831 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:04:05.831 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 15:04:05.831 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 15:04:05.831 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:04:05.839 | 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

2025-09-01 15:04:05.840 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:04:05.930 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:04:06.013 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch326
2025-09-01 15:04:08.998 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 326/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 8.687e-04, size: 576, ETA: 1:32:03
2025-09-01 15:04:12.030 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 326/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.7, lr: 8.679e-04, size: 416, ETA: 1:32:00
2025-09-01 15:04:15.144 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 326/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 4.4, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.8, cls_loss: 0.5, lr: 8.671e-04, size: 256, ETA: 1:31:57
2025-09-01 15:04:18.127 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 326/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, 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: 8.663e-04, size: 448, ETA: 1:31:54
2025-09-01 15:04:21.129 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 326/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, 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: 8.655e-04, size: 384, ETA: 1:31:50
2025-09-01 15:04:24.281 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 326/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.0, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.6, lr: 8.646e-04, size: 576, ETA: 1:31:47
2025-09-01 15:04:25.836 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:04:32.206 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:04:33.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:04:33.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5827
2025-09-01 15:04:33.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4887
2025-09-01 15:04:33.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3754
2025-09-01 15:04:33.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4823
2025-09-01 15:04:33.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:04:33.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:04:33.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.583
2025-09-01 15:04:33.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-09-01 15:04:33.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-09-01 15:04:33.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.482
2025-09-01 15:04:33.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:04:33.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:04:33.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:04:33.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:04:33.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:04:33.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:04:33.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:04:33.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:04:33.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:04:34.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:04:35.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:04:35.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:04:36.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:04:37.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:04:37.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:04:38.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:04:39.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:04:40.104 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:04:40.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:04:40.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:04:40.105 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:04:40.112 | 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

2025-09-01 15:04:40.113 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:04:40.277 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:04:40.350 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch327
2025-09-01 15:04:43.104 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 327/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.136s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 2.2, cls_loss: 0.5, lr: 8.635e-04, size: 448, ETA: 1:31:43
2025-09-01 15:04:46.191 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 327/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 8.627e-04, size: 576, ETA: 1:31:39
2025-09-01 15:04:49.369 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 327/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 8.619e-04, size: 576, ETA: 1:31:36
2025-09-01 15:04:52.481 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 327/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, 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: 8.611e-04, size: 352, ETA: 1:31:33
2025-09-01 15:04:55.471 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 327/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 8.603e-04, size: 480, ETA: 1:31:30
2025-09-01 15:04:58.484 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 327/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 8.595e-04, size: 256, ETA: 1:31:27
2025-09-01 15:04:59.773 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:05:06.145 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:05:07.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:05:08.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5795
2025-09-01 15:05:08.430 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5071
2025-09-01 15:05:08.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3645
2025-09-01 15:05:08.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4837
2025-09-01 15:05:08.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:05:08.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:05:08.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.579
2025-09-01 15:05:08.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-09-01 15:05:08.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-09-01 15:05:08.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-09-01 15:05:08.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:05:08.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:05:08.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:05:08.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:05:08.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:05:08.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:05:08.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:05:08.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:05:08.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:05:09.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:05:10.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:05:11.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:05:12.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:05:13.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:05:14.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:05:15.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:05:17.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:05:18.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:05:18.126 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:05:18.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:05:18.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:05:18.136 | 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

2025-09-01 15:05:18.137 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:05:18.254 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:05:18.372 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch328
2025-09-01 15:05:21.317 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 328/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.1, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.6, lr: 8.583e-04, size: 256, ETA: 1:31:22
2025-09-01 15:05:24.280 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 328/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 8.575e-04, size: 416, ETA: 1:31:19
2025-09-01 15:05:27.411 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 328/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.7, lr: 8.567e-04, size: 416, ETA: 1:31:16
2025-09-01 15:05:30.417 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 328/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 8.559e-04, size: 416, ETA: 1:31:12
2025-09-01 15:05:33.512 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 328/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, 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: 8.551e-04, size: 576, ETA: 1:31:09
2025-09-01 15:05:36.550 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 328/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.9, lr: 8.543e-04, size: 512, ETA: 1:31:06
2025-09-01 15:05:37.926 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:05:44.247 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:05:45.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:05:45.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6053
2025-09-01 15:05:45.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5148
2025-09-01 15:05:45.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3578
2025-09-01 15:05:45.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4926
2025-09-01 15:05:45.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:05:45.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:05:45.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.605
2025-09-01 15:05:45.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 15:05:45.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-09-01 15:05:45.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-09-01 15:05:45.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:05:45.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:05:45.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:05:45.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:05:45.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:05:45.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:05:45.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:05:45.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:05:45.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:05:46.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:05:47.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:05:47.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:05:48.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:05:49.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:05:50.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:05:50.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:05:51.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:05:52.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:05:52.084 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:05:52.085 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:05:52.085 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:05:52.092 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.08 ms, Average NMS time: 0.92 ms, Average inference time: 7.00 ms

2025-09-01 15:05:52.093 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:05:52.182 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:05:52.262 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch329
2025-09-01 15:05:55.172 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 329/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 8.531e-04, size: 320, ETA: 1:31:01
2025-09-01 15:05:58.176 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 329/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 8.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.9, cls_loss: 0.9, lr: 8.523e-04, size: 320, ETA: 1:30:58
2025-09-01 15:06:01.354 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 329/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.157s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.8, lr: 8.515e-04, size: 352, ETA: 1:30:55
2025-09-01 15:06:04.345 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 329/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.8, lr: 8.507e-04, size: 288, ETA: 1:30:52
2025-09-01 15:06:07.302 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 329/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, 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: 8.499e-04, size: 544, ETA: 1:30:48
2025-09-01 15:06:10.362 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 329/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 8.491e-04, size: 576, ETA: 1:30:45
2025-09-01 15:06:11.772 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:06:18.012 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:06:18.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:06:19.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5973
2025-09-01 15:06:19.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5134
2025-09-01 15:06:19.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3774
2025-09-01 15:06:19.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4960
2025-09-01 15:06:19.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:06:19.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:06:19.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.597
2025-09-01 15:06:19.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-09-01 15:06:19.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-09-01 15:06:19.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.496
2025-09-01 15:06:19.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:06:19.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:06:19.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:06:19.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:06:19.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:06:19.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:06:19.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:06:19.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:06:19.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:06:20.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:06:20.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:06:21.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:06:22.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:06:22.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:06:23.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:06:24.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:06:24.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:06:25.272 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:06:25.273 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:06:25.273 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:06:25.273 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:06:25.281 | 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

2025-09-01 15:06:25.282 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:06:25.367 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:06:25.503 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch330
2025-09-01 15:06:28.426 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 330/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.8, lr: 8.479e-04, size: 288, ETA: 1:30:40
2025-09-01 15:06:31.404 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 330/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 9.0, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 3.9, cls_loss: 0.7, lr: 8.471e-04, size: 544, ETA: 1:30:37
2025-09-01 15:06:34.456 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 330/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 4.3, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.6, lr: 8.463e-04, size: 288, ETA: 1:30:34
2025-09-01 15:06:37.362 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 330/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 8.455e-04, size: 384, ETA: 1:30:31
2025-09-01 15:06:40.310 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 330/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 3.4, cls_loss: 0.8, lr: 8.447e-04, size: 576, ETA: 1:30:27
2025-09-01 15:06:43.336 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 330/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 8.439e-04, size: 288, ETA: 1:30:24
2025-09-01 15:06:44.662 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:06:50.880 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:06:51.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:06:52.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5947
2025-09-01 15:06:52.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4850
2025-09-01 15:06:52.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3362
2025-09-01 15:06:52.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4719
2025-09-01 15:06:52.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:06:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:06:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.595
2025-09-01 15:06:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.485
2025-09-01 15:06:52.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.336
2025-09-01 15:06:52.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.472
2025-09-01 15:06:52.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:06:52.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:06:52.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:06:52.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:06:52.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:06:52.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:06:52.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:06:52.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:06:52.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:06:53.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:06:54.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:06:55.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:06:55.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:06:56.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:06:57.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:06:58.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:06:59.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:07:00.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:07:00.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:07:00.072 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 15:07:00.072 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:07:00.079 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.34 ms, Average NMS time: 0.94 ms, Average inference time: 7.28 ms

2025-09-01 15:07:00.081 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:07:00.174 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:07:00.254 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch331
2025-09-01 15:07:03.034 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 331/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.137s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 8.428e-04, size: 480, ETA: 1:30:19
2025-09-01 15:07:06.003 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 331/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.7, lr: 8.420e-04, size: 352, ETA: 1:30:16
2025-09-01 15:07:09.185 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 331/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.157s, data_time: 0.006s, total_loss: 7.7, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 1.0, lr: 8.412e-04, size: 576, ETA: 1:30:13
2025-09-01 15:07:12.328 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 331/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.6, lr: 8.404e-04, size: 480, ETA: 1:30:10
2025-09-01 15:07:15.410 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 331/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.6, lr: 8.396e-04, size: 512, ETA: 1:30:07
2025-09-01 15:07:18.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 331/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 8.388e-04, size: 256, ETA: 1:30:03
2025-09-01 15:07:19.817 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:07:26.007 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:07:26.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:07:27.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5861
2025-09-01 15:07:27.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5162
2025-09-01 15:07:27.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3558
2025-09-01 15:07:27.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4860
2025-09-01 15:07:27.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:07:27.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:07:27.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-09-01 15:07:27.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-09-01 15:07:27.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-09-01 15:07:27.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.486
2025-09-01 15:07:27.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:07:27.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:07:27.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:07:27.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:07:27.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:07:27.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:07:27.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:07:27.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:07:27.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:07:28.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:07:29.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:07:30.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:07:30.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:07:31.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:07:32.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:07:33.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:07:34.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:07:34.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:07:34.866 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:07:34.866 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:07:34.867 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:07:34.880 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.38 ms, Average NMS time: 0.93 ms, Average inference time: 7.31 ms

2025-09-01 15:07:34.881 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:07:35.098 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:07:35.197 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch332
2025-09-01 15:07:38.325 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 332/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.6, lr: 8.376e-04, size: 320, ETA: 1:29:59
2025-09-01 15:07:41.346 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 332/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 8.368e-04, size: 384, ETA: 1:29:56
2025-09-01 15:07:44.331 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 332/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 8.360e-04, size: 256, ETA: 1:29:52
2025-09-01 15:07:47.437 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 332/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.6, lr: 8.352e-04, size: 416, ETA: 1:29:49
2025-09-01 15:07:50.477 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 332/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.7, lr: 8.344e-04, size: 320, ETA: 1:29:46
2025-09-01 15:07:53.575 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 332/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.8, lr: 8.336e-04, size: 480, ETA: 1:29:43
2025-09-01 15:07:54.932 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:08:01.179 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:08:01.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:08:02.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5871
2025-09-01 15:08:02.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4994
2025-09-01 15:08:02.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3546
2025-09-01 15:08:02.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4803
2025-09-01 15:08:02.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:08:02.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:08:02.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.587
2025-09-01 15:08:02.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-09-01 15:08:02.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.355
2025-09-01 15:08:02.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.480
2025-09-01 15:08:02.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:08:02.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:08:02.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:08:02.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:08:02.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:08:02.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:08:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:08:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:08:02.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:08:02.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:08:03.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:08:03.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:08:04.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:08:04.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:08:05.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:08:05.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:08:06.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:08:06.587 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:08:06.587 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:08:06.588 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:08:06.588 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:08:06.594 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.17 ms, Average NMS time: 0.84 ms, Average inference time: 7.00 ms

2025-09-01 15:08:06.596 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:08:06.743 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:08:06.815 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch333
2025-09-01 15:08:09.689 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 333/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.5, lr: 8.324e-04, size: 416, ETA: 1:29:38
2025-09-01 15:08:12.724 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 333/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.6, lr: 8.316e-04, size: 544, ETA: 1:29:35
2025-09-01 15:08:15.781 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 333/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 8.308e-04, size: 352, ETA: 1:29:32
2025-09-01 15:08:18.810 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 333/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.2, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 0.8, cls_loss: 0.6, lr: 8.300e-04, size: 544, ETA: 1:29:28
2025-09-01 15:08:21.818 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 333/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, 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: 8.292e-04, size: 384, ETA: 1:29:25
2025-09-01 15:08:24.829 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 333/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.7, lr: 8.284e-04, size: 256, ETA: 1:29:22
2025-09-01 15:08:26.135 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:08:32.243 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:08:33.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:08:33.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5888
2025-09-01 15:08:33.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5103
2025-09-01 15:08:33.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3436
2025-09-01 15:08:33.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4809
2025-09-01 15:08:33.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:08:33.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:08:33.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-09-01 15:08:33.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.510
2025-09-01 15:08:33.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.344
2025-09-01 15:08:33.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-09-01 15:08:33.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:08:33.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:08:33.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:08:33.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:08:33.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:08:33.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:08:33.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:08:33.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:08:33.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:08:34.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:08:35.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:08:35.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:08:36.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:08:37.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:08:37.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:08:38.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:08:38.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:08:39.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:08:39.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:08:39.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:08:39.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:08:39.584 | 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

2025-09-01 15:08:39.584 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:08:39.672 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:08:39.754 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch334
2025-09-01 15:08:42.695 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 334/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, 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: 8.273e-04, size: 352, ETA: 1:29:17
2025-09-01 15:08:45.652 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 334/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 8.265e-04, size: 544, ETA: 1:29:14
2025-09-01 15:08:48.725 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 334/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.006s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.7, lr: 8.257e-04, size: 416, ETA: 1:29:11
2025-09-01 15:08:52.028 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 334/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 2.4, cls_loss: 0.7, lr: 8.249e-04, size: 576, ETA: 1:29:08
2025-09-01 15:08:55.188 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 334/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 3.1, iou_loss: 1.3, l1_loss: 0.4, conf_loss: 1.0, cls_loss: 0.5, lr: 8.241e-04, size: 288, ETA: 1:29:05
2025-09-01 15:08:58.188 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 334/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.9, lr: 8.233e-04, size: 320, ETA: 1:29:01
2025-09-01 15:08:59.526 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:09:05.717 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:09:06.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:09:07.141 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6010
2025-09-01 15:09:07.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5233
2025-09-01 15:09:07.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3969
2025-09-01 15:09:07.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5071
2025-09-01 15:09:07.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:09:07.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:09:07.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.601
2025-09-01 15:09:07.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-09-01 15:09:07.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-09-01 15:09:07.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.507
2025-09-01 15:09:07.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:09:07.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:09:07.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:09:07.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:09:07.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:09:07.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:09:07.324 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:09:07.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:09:07.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:09:08.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:09:08.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:09:09.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:09:10.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:09:10.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:09:11.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:09:12.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:09:13.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:09:13.760 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:09:13.760 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:09:13.760 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:09:13.761 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:09:13.768 | 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

2025-09-01 15:09:13.769 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:09:13.850 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:09:13.942 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch335
2025-09-01 15:09:16.813 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 335/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 1.7, cls_loss: 0.7, lr: 8.221e-04, size: 480, ETA: 1:28:57
2025-09-01 15:09:19.873 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 335/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 8.213e-04, size: 544, ETA: 1:28:53
2025-09-01 15:09:22.962 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 335/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.6, lr: 8.205e-04, size: 576, ETA: 1:28:50
2025-09-01 15:09:26.079 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 335/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 8.6, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 4.0, cls_loss: 0.7, lr: 8.197e-04, size: 320, ETA: 1:28:47
2025-09-01 15:09:29.050 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 335/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 8.2, iou_loss: 3.3, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.7, lr: 8.189e-04, size: 416, ETA: 1:28:44
2025-09-01 15:09:32.151 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 335/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.7, lr: 8.181e-04, size: 544, ETA: 1:28:41
2025-09-01 15:09:33.612 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:09:39.832 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:09:40.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:09:40.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5805
2025-09-01 15:09:40.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5115
2025-09-01 15:09:41.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3866
2025-09-01 15:09:41.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4929
2025-09-01 15:09:41.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:09:41.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:09:41.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.581
2025-09-01 15:09:41.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-09-01 15:09:41.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-09-01 15:09:41.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-09-01 15:09:41.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:09:41.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:09:41.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:09:41.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:09:41.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:09:41.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:09:41.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:09:41.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:09:41.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:09:41.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:09:42.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:09:42.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:09:43.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:09:43.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:09:44.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:09:44.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:09:45.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:09:45.879 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:09:45.879 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:09:45.879 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:09:45.879 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:09:45.886 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.90 ms, Average inference time: 6.99 ms

2025-09-01 15:09:45.887 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:09:45.969 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:09:46.051 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch336
2025-09-01 15:09:48.914 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 336/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 8.170e-04, size: 352, ETA: 1:28:36
2025-09-01 15:09:51.895 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 336/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.7, lr: 8.162e-04, size: 320, ETA: 1:28:33
2025-09-01 15:09:55.103 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 336/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 1.6, cls_loss: 0.7, lr: 8.154e-04, size: 512, ETA: 1:28:30
2025-09-01 15:09:58.235 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 336/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 8.146e-04, size: 320, ETA: 1:28:26
2025-09-01 15:10:01.215 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 336/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 3.0, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 0.8, cls_loss: 0.4, lr: 8.138e-04, size: 320, ETA: 1:28:23
2025-09-01 15:10:04.200 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 336/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 0.7, lr: 8.130e-04, size: 352, ETA: 1:28:20
2025-09-01 15:10:05.520 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:10:11.907 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:10:12.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:10:13.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6083
2025-09-01 15:10:13.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5240
2025-09-01 15:10:13.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3959
2025-09-01 15:10:13.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5094
2025-09-01 15:10:13.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:10:13.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:10:13.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-09-01 15:10:13.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-09-01 15:10:13.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-09-01 15:10:13.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.509
2025-09-01 15:10:13.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:10:13.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:10:13.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:10:13.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:10:13.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:10:13.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:10:13.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:10:13.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:10:13.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:10:14.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:10:15.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:10:15.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:10:16.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:10:17.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:10:18.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:10:19.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:10:19.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:10:20.601 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:10:20.601 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 15:10:20.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:10:20.602 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:10:20.616 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.09 ms, Average NMS time: 0.88 ms, Average inference time: 6.97 ms

2025-09-01 15:10:20.617 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:10:20.736 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:10:20.898 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch337
2025-09-01 15:10:23.819 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 337/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.8, lr: 8.118e-04, size: 544, ETA: 1:28:15
2025-09-01 15:10:26.873 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 337/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 8.110e-04, size: 352, ETA: 1:28:12
2025-09-01 15:10:29.869 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 337/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 8.102e-04, size: 480, ETA: 1:28:09
2025-09-01 15:10:32.831 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 337/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, 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: 0.7, lr: 8.094e-04, size: 288, ETA: 1:28:06
2025-09-01 15:10:35.898 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 337/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.2, cls_loss: 0.6, lr: 8.086e-04, size: 480, ETA: 1:28:02
2025-09-01 15:10:38.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 337/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, 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: 8.078e-04, size: 544, ETA: 1:27:59
2025-09-01 15:10:40.319 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:10:46.497 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:10:47.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:10:48.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5447
2025-09-01 15:10:48.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4788
2025-09-01 15:10:49.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3658
2025-09-01 15:10:49.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4631
2025-09-01 15:10:49.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:10:49.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:10:49.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-09-01 15:10:49.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.479
2025-09-01 15:10:49.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.366
2025-09-01 15:10:49.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.463
2025-09-01 15:10:49.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:10:49.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:10:49.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:10:49.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:10:49.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:10:49.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:10:49.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:10:49.015 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:10:49.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:10:50.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:10:51.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:10:52.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:10:53.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:10:54.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:10:56.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:10:57.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:10:58.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:10:59.803 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:10:59.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:10:59.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 15:10:59.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:10:59.811 | 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

2025-09-01 15:10:59.817 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:10:59.905 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:10:59.986 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch338
2025-09-01 15:11:02.811 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 338/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.7, lr: 8.067e-04, size: 384, ETA: 1:27:54
2025-09-01 15:11:05.890 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 338/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, 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: 8.059e-04, size: 320, ETA: 1:27:51
2025-09-01 15:11:08.845 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 338/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.005s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 8.051e-04, size: 320, ETA: 1:27:48
2025-09-01 15:11:11.983 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 338/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, 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: 8.043e-04, size: 544, ETA: 1:27:45
2025-09-01 15:11:15.034 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 338/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.8, lr: 8.035e-04, size: 288, ETA: 1:27:42
2025-09-01 15:11:18.032 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 338/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.8, lr: 8.027e-04, size: 320, ETA: 1:27:38
2025-09-01 15:11:19.408 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:11:25.760 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:11:26.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:11:27.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6051
2025-09-01 15:11:27.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5167
2025-09-01 15:11:27.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3758
2025-09-01 15:11:27.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4992
2025-09-01 15:11:27.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:11:27.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:11:27.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.605
2025-09-01 15:11:27.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 15:11:27.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-09-01 15:11:27.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.499
2025-09-01 15:11:27.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:11:27.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:11:27.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:11:27.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:11:27.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:11:27.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:11:27.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:11:27.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:11:27.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:11:28.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:11:28.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:11:29.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:11:30.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:11:31.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:11:31.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:11:32.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:11:33.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:11:33.936 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:11:33.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:11:33.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:11:33.937 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:11:33.946 | 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

2025-09-01 15:11:33.950 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:11:34.038 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:11:34.166 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch339
2025-09-01 15:11:37.113 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 339/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 1.8, cls_loss: 0.7, lr: 8.015e-04, size: 544, ETA: 1:27:34
2025-09-01 15:11:40.112 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 339/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, 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: 8.008e-04, size: 480, ETA: 1:27:30
2025-09-01 15:11:43.175 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 339/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 8.000e-04, size: 480, ETA: 1:27:27
2025-09-01 15:11:46.305 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 339/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 7.992e-04, size: 256, ETA: 1:27:24
2025-09-01 15:11:49.210 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 339/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 7.984e-04, size: 384, ETA: 1:27:21
2025-09-01 15:11:52.213 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 339/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 3.7, cls_loss: 0.7, lr: 7.976e-04, size: 544, ETA: 1:27:18
2025-09-01 15:11:53.626 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:11:59.909 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:12:00.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:12:01.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5785
2025-09-01 15:12:01.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4900
2025-09-01 15:12:01.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3430
2025-09-01 15:12:01.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4705
2025-09-01 15:12:01.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:12:01.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:12:01.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-09-01 15:12:01.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-09-01 15:12:01.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.343
2025-09-01 15:12:01.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.471
2025-09-01 15:12:01.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:12:01.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:12:01.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:12:01.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:12:01.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:12:01.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:12:01.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:12:01.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:12:01.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:12:02.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:12:03.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:12:04.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:12:04.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:12:05.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:12:06.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:12:07.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:12:08.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:12:08.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:12:08.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:12:08.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 15:12:08.976 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:12:08.984 | 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: 7.09 ms

2025-09-01 15:12:08.985 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:12:09.072 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:12:09.195 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch340
2025-09-01 15:12:12.171 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 340/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 7.964e-04, size: 256, ETA: 1:27:13
2025-09-01 15:12:15.218 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 340/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.6, lr: 7.956e-04, size: 416, ETA: 1:27:10
2025-09-01 15:12:18.217 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 340/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, 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: 7.948e-04, size: 512, ETA: 1:27:06
2025-09-01 15:12:21.227 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 340/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 8.3, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.7, cls_loss: 0.9, lr: 7.940e-04, size: 576, ETA: 1:27:03
2025-09-01 15:12:24.380 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 340/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 9.3, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 3.9, cls_loss: 1.0, lr: 7.932e-04, size: 352, ETA: 1:27:00
2025-09-01 15:12:27.358 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 340/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 2.1, cls_loss: 0.7, lr: 7.924e-04, size: 480, ETA: 1:26:57
2025-09-01 15:12:28.699 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:12:35.000 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:12:35.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:12:36.489 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5979
2025-09-01 15:12:36.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5241
2025-09-01 15:12:36.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3545
2025-09-01 15:12:36.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4922
2025-09-01 15:12:36.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:12:36.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:12:36.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-09-01 15:12:36.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-09-01 15:12:36.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.354
2025-09-01 15:12:36.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-09-01 15:12:36.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:12:36.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:12:36.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:12:36.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:12:36.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:12:36.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:12:36.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:12:36.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:12:36.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:12:37.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:12:38.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:12:38.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:12:39.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:12:40.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:12:41.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:12:42.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:12:42.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:12:43.538 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:12:43.539 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:12:43.539 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:12:43.539 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:12:43.546 | 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: 7.05 ms

2025-09-01 15:12:43.547 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:12:43.672 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:12:43.788 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch341
2025-09-01 15:12:46.647 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 341/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 7.913e-04, size: 448, ETA: 1:26:52
2025-09-01 15:12:49.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 341/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 9.6, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 5.3, cls_loss: 0.6, lr: 7.905e-04, size: 448, ETA: 1:26:49
2025-09-01 15:12:52.773 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 341/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, 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: 7.897e-04, size: 288, ETA: 1:26:46
2025-09-01 15:12:55.882 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 341/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 2.8, cls_loss: 0.6, lr: 7.889e-04, size: 352, ETA: 1:26:43
2025-09-01 15:12:59.066 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 341/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 7.881e-04, size: 512, ETA: 1:26:40
2025-09-01 15:13:02.046 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 341/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 7.873e-04, size: 480, ETA: 1:26:36
2025-09-01 15:13:03.435 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:13:09.561 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:13:10.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:13:10.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5856
2025-09-01 15:13:10.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5038
2025-09-01 15:13:11.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3704
2025-09-01 15:13:11.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4866
2025-09-01 15:13:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:13:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:13:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.586
2025-09-01 15:13:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-09-01 15:13:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-09-01 15:13:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-09-01 15:13:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:13:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:13:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:13:11.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:13:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:13:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:13:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:13:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:13:11.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:13:11.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:13:12.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:13:13.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:13:13.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:13:14.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:13:15.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:13:15.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:13:16.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:13:17.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:13:17.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:13:17.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:13:17.034 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:13:17.044 | 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

2025-09-01 15:13:17.050 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:13:17.175 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:13:17.249 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch342
2025-09-01 15:13:20.084 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 342/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.9, lr: 7.862e-04, size: 288, ETA: 1:26:32
2025-09-01 15:13:23.074 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 342/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.6, lr: 7.854e-04, size: 480, ETA: 1:26:28
2025-09-01 15:13:26.155 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 342/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 7.846e-04, size: 544, ETA: 1:26:25
2025-09-01 15:13:29.195 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 342/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 7.838e-04, size: 416, ETA: 1:26:22
2025-09-01 15:13:32.201 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 342/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, 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: 7.830e-04, size: 480, ETA: 1:26:19
2025-09-01 15:13:35.379 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 342/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.1, l1_loss: 1.4, conf_loss: 3.3, cls_loss: 1.0, lr: 7.822e-04, size: 576, ETA: 1:26:16
2025-09-01 15:13:36.758 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:13:42.890 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:13:44.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:13:44.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6036
2025-09-01 15:13:45.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5239
2025-09-01 15:13:45.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3380
2025-09-01 15:13:45.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4885
2025-09-01 15:13:45.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:13:45.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:13:45.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-09-01 15:13:45.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-09-01 15:13:45.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.338
2025-09-01 15:13:45.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.489
2025-09-01 15:13:45.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:13:45.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:13:45.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:13:45.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:13:45.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:13:45.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:13:45.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:13:45.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:13:45.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:13:46.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:13:47.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:13:48.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:13:49.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:13:50.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:13:51.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:13:52.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:13:53.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:13:54.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:13:54.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:13:54.951 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:13:54.952 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:13:54.960 | 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

2025-09-01 15:13:54.962 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:13:55.051 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:13:55.174 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch343
2025-09-01 15:13:58.031 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 343/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.8, lr: 7.811e-04, size: 416, ETA: 1:26:11
2025-09-01 15:14:01.046 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 343/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 7.803e-04, size: 544, ETA: 1:26:08
2025-09-01 15:14:04.193 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 343/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.5, lr: 7.795e-04, size: 576, ETA: 1:26:05
2025-09-01 15:14:07.181 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 343/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, 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: 320, ETA: 1:26:01
2025-09-01 15:14:10.152 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 343/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.5, lr: 7.779e-04, size: 352, ETA: 1:25:58
2025-09-01 15:14:13.142 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 343/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, 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: 7.771e-04, size: 512, ETA: 1:25:55
2025-09-01 15:14:14.499 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:14:20.498 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:14:21.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:14:22.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6041
2025-09-01 15:14:22.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5015
2025-09-01 15:14:22.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3328
2025-09-01 15:14:22.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4795
2025-09-01 15:14:22.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:14:22.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:14:22.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-09-01 15:14:22.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-09-01 15:14:22.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-09-01 15:14:22.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.479
2025-09-01 15:14:22.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:14:22.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:14:22.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:14:22.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:14:22.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:14:22.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:14:22.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:14:22.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:14:22.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:14:23.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:14:24.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:14:25.238 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:14:26.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:14:27.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:14:27.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:14:28.895 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:14:29.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:14:30.752 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:14:30.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 15:14:30.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:14:30.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:14:30.760 | 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

2025-09-01 15:14:30.764 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:14:30.846 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:14:30.928 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch344
2025-09-01 15:14:33.933 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 344/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 7.760e-04, size: 480, ETA: 1:25:50
2025-09-01 15:14:36.961 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 344/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, 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: 7.752e-04, size: 384, ETA: 1:25:47
2025-09-01 15:14:40.020 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 344/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.5, lr: 7.744e-04, size: 352, ETA: 1:25:44
2025-09-01 15:14:43.065 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 344/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.6, lr: 7.736e-04, size: 512, ETA: 1:25:41
2025-09-01 15:14:46.201 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 344/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.7, lr: 7.728e-04, size: 384, ETA: 1:25:38
2025-09-01 15:14:49.169 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 344/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 2.9, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.4, lr: 7.720e-04, size: 480, ETA: 1:25:34
2025-09-01 15:14:50.564 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:14:56.843 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:14:57.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:14:58.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5978
2025-09-01 15:14:58.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5171
2025-09-01 15:14:58.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3947
2025-09-01 15:14:58.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5032
2025-09-01 15:14:58.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:14:58.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:14:58.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-09-01 15:14:58.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 15:14:58.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-09-01 15:14:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-09-01 15:14:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:14:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:14:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:14:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:14:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:14:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:14:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:14:58.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:14:58.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:14:58.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:14:59.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:15:00.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:15:00.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:15:01.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:15:02.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:15:02.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:15:03.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:15:03.899 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:15:03.900 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:15:03.900 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:15:03.900 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:15:03.907 | 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: 7.30 ms

2025-09-01 15:15:03.913 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:15:03.993 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:15:04.076 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch345
2025-09-01 15:15:06.899 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 345/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.6, lr: 7.709e-04, size: 416, ETA: 1:25:30
2025-09-01 15:15:10.021 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 345/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.7, lr: 7.701e-04, size: 448, ETA: 1:25:26
2025-09-01 15:15:13.177 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 345/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.005s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 1.0, lr: 7.693e-04, size: 448, ETA: 1:25:23
2025-09-01 15:15:16.296 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 345/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 7.685e-04, size: 384, ETA: 1:25:20
2025-09-01 15:15:19.299 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 345/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 7.677e-04, size: 480, ETA: 1:25:17
2025-09-01 15:15:22.386 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 345/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, 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: 7.669e-04, size: 512, ETA: 1:25:14
2025-09-01 15:15:23.783 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:15:30.135 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:15:31.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:15:32.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5919
2025-09-01 15:15:32.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4895
2025-09-01 15:15:32.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3615
2025-09-01 15:15:32.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4810
2025-09-01 15:15:32.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:15:32.289 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:15:32.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-09-01 15:15:32.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-09-01 15:15:32.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-09-01 15:15:32.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.481
2025-09-01 15:15:32.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:15:32.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:15:32.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:15:32.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:15:32.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:15:32.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:15:32.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:15:32.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:15:32.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:15:33.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:15:34.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:15:35.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:15:36.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:15:37.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:15:38.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:15:39.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:15:40.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:15:41.259 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:15:41.259 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:15:41.259 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:15:41.259 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:15:41.266 | 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: 7.13 ms

2025-09-01 15:15:41.267 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:15:41.368 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:15:41.453 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch346
2025-09-01 15:15:44.333 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 346/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 7.658e-04, size: 480, ETA: 1:25:09
2025-09-01 15:15:47.310 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 346/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.6, lr: 7.650e-04, size: 544, ETA: 1:25:06
2025-09-01 15:15:50.303 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 346/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, 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: 7.642e-04, size: 416, ETA: 1:25:03
2025-09-01 15:15:53.356 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 346/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 7.634e-04, size: 384, ETA: 1:24:59
2025-09-01 15:15:56.385 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 346/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.0, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.6, lr: 7.626e-04, size: 576, ETA: 1:24:56
2025-09-01 15:15:59.423 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 346/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 7.618e-04, size: 288, ETA: 1:24:53
2025-09-01 15:16:00.829 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:16:07.157 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:16:08.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:16:09.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5934
2025-09-01 15:16:09.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4955
2025-09-01 15:16:09.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3169
2025-09-01 15:16:09.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4686
2025-09-01 15:16:09.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:16:09.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:16:09.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-09-01 15:16:09.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-09-01 15:16:09.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.317
2025-09-01 15:16:09.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.469
2025-09-01 15:16:09.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:16:09.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:16:09.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:16:09.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:16:09.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:16:09.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:16:09.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:16:09.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:16:09.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:16:10.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:16:12.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:16:13.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:16:14.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:16:15.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:16:16.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:16:17.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:16:18.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:16:20.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:16:20.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:16:20.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 15:16:20.112 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:16:20.120 | 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

2025-09-01 15:16:20.122 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:16:20.203 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:16:20.286 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch347
2025-09-01 15:16:23.202 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 347/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.7, lr: 7.607e-04, size: 544, ETA: 1:24:48
2025-09-01 15:16:26.303 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 347/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 2.9, cls_loss: 0.7, lr: 7.599e-04, size: 576, ETA: 1:24:45
2025-09-01 15:16:29.499 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 347/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.8, lr: 7.591e-04, size: 320, ETA: 1:24:42
2025-09-01 15:16:32.443 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 347/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.5, lr: 7.583e-04, size: 352, ETA: 1:24:39
2025-09-01 15:16:35.472 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 347/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 3.4, cls_loss: 0.9, lr: 7.575e-04, size: 544, ETA: 1:24:36
2025-09-01 15:16:38.419 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 347/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.2, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.6, lr: 7.567e-04, size: 352, ETA: 1:24:32
2025-09-01 15:16:39.770 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:16:45.820 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:16:46.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:16:47.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5823
2025-09-01 15:16:47.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5106
2025-09-01 15:16:47.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3590
2025-09-01 15:16:47.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4840
2025-09-01 15:16:47.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:16:47.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:16:47.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-09-01 15:16:47.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-09-01 15:16:47.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-09-01 15:16:47.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.484
2025-09-01 15:16:47.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:16:47.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:16:47.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:16:47.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:16:47.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:16:47.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:16:47.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:16:47.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:16:47.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:16:48.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:16:49.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:16:49.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:16:50.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:16:51.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:16:51.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:16:52.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:16:53.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:16:54.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:16:54.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:16:54.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:16:54.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:16:54.240 | 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

2025-09-01 15:16:54.242 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:16:54.332 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:16:54.413 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch348
2025-09-01 15:16:57.365 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 348/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 3.1, cls_loss: 0.7, lr: 7.556e-04, size: 480, ETA: 1:24:28
2025-09-01 15:17:00.342 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 348/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 7.548e-04, size: 352, ETA: 1:24:25
2025-09-01 15:17:03.436 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 348/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 7.540e-04, size: 480, ETA: 1:24:21
2025-09-01 15:17:06.502 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 348/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 7.532e-04, size: 544, ETA: 1:24:18
2025-09-01 15:17:09.532 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 348/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 7.525e-04, size: 352, ETA: 1:24:15
2025-09-01 15:17:12.574 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 348/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 7.517e-04, size: 288, ETA: 1:24:12
2025-09-01 15:17:13.961 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:17:20.225 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:17:21.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:17:21.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5983
2025-09-01 15:17:22.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5247
2025-09-01 15:17:22.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3522
2025-09-01 15:17:22.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4917
2025-09-01 15:17:22.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:17:22.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:17:22.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-09-01 15:17:22.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-09-01 15:17:22.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.352
2025-09-01 15:17:22.048 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-09-01 15:17:22.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:17:22.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:17:22.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:17:22.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:17:22.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:17:22.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:17:22.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:17:22.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:17:22.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:17:22.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:17:23.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:17:24.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:17:25.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:17:26.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:17:27.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:17:28.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:17:28.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:17:29.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:17:29.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:17:29.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:17:29.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:17:29.857 | 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

2025-09-01 15:17:29.859 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:17:29.948 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:17:30.029 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch349
2025-09-01 15:17:32.915 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 349/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.143s, 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: 7.505e-04, size: 480, ETA: 1:24:07
2025-09-01 15:17:36.105 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 349/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 7.497e-04, size: 320, ETA: 1:24:04
2025-09-01 15:17:39.078 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 349/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.8, lr: 7.490e-04, size: 416, ETA: 1:24:01
2025-09-01 15:17:42.081 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 349/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.2, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 7.482e-04, size: 480, ETA: 1:23:58
2025-09-01 15:17:45.255 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 349/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 7.474e-04, size: 576, ETA: 1:23:54
2025-09-01 15:17:48.372 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 349/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 9.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 5.2, cls_loss: 0.7, lr: 7.466e-04, size: 448, ETA: 1:23:51
2025-09-01 15:17:49.745 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:17:55.968 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:17:56.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:17:57.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5739
2025-09-01 15:17:57.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5023
2025-09-01 15:17:57.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3639
2025-09-01 15:17:57.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4801
2025-09-01 15:17:57.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:17:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:17:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-09-01 15:17:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-09-01 15:17:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-09-01 15:17:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.480
2025-09-01 15:17:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:17:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:17:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:17:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:17:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:17:57.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:17:57.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:17:57.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:17:57.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:17:58.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:17:58.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:17:59.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:17:59.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:18:00.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:18:01.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:18:01.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:18:02.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:18:03.175 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:18:03.175 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:18:03.175 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:18:03.175 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:18:03.183 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.10 ms, Average NMS time: 0.89 ms, Average inference time: 6.99 ms

2025-09-01 15:18:03.184 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:18:03.271 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:18:03.352 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch350
2025-09-01 15:18:06.247 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 350/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.8, lr: 7.455e-04, size: 480, ETA: 1:23:47
2025-09-01 15:18:09.321 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 350/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 7.447e-04, size: 544, ETA: 1:23:43
2025-09-01 15:18:12.409 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 350/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.004s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 7.439e-04, size: 384, ETA: 1:23:40
2025-09-01 15:18:15.367 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 350/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.7, lr: 7.431e-04, size: 512, ETA: 1:23:37
2025-09-01 15:18:18.585 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 350/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 7.423e-04, size: 544, ETA: 1:23:34
2025-09-01 15:18:21.542 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 350/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 7.415e-04, size: 256, ETA: 1:23:31
2025-09-01 15:18:22.888 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:18:28.984 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:18:30.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:18:31.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5961
2025-09-01 15:18:31.351 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5105
2025-09-01 15:18:31.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3835
2025-09-01 15:18:31.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4967
2025-09-01 15:18:31.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:18:31.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:18:31.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.596
2025-09-01 15:18:31.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.510
2025-09-01 15:18:31.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-09-01 15:18:31.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-09-01 15:18:31.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:18:31.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:18:31.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:18:31.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:18:31.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:18:31.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:18:31.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:18:31.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:18:31.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:18:32.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:18:33.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:18:34.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:18:36.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:18:37.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:18:38.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:18:39.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:18:40.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:18:41.754 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:18:41.754 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:18:41.754 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:18:41.755 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:18:41.762 | 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

2025-09-01 15:18:41.764 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:18:41.881 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:18:41.958 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch351
2025-09-01 15:18:44.784 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 351/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 0.8, conf_loss: 3.4, cls_loss: 0.7, lr: 7.404e-04, size: 416, ETA: 1:23:26
2025-09-01 15:18:47.853 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 351/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.7, lr: 7.396e-04, size: 352, ETA: 1:23:23
2025-09-01 15:18:50.963 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 351/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 7.388e-04, size: 288, ETA: 1:23:20
2025-09-01 15:18:54.086 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 351/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.7, l1_loss: 0.9, conf_loss: 1.0, cls_loss: 0.5, lr: 7.380e-04, size: 576, ETA: 1:23:17
2025-09-01 15:18:57.213 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 351/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 7.373e-04, size: 480, ETA: 1:23:13
2025-09-01 15:19:00.470 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 351/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 7.365e-04, size: 256, ETA: 1:23:10
2025-09-01 15:19:02.038 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:19:08.335 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:19:09.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:19:09.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5906
2025-09-01 15:19:09.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5082
2025-09-01 15:19:09.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3652
2025-09-01 15:19:09.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4880
2025-09-01 15:19:09.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:19:09.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:19:09.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.591
2025-09-01 15:19:09.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.508
2025-09-01 15:19:09.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-09-01 15:19:09.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.488
2025-09-01 15:19:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:19:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:19:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:19:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:19:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:19:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:19:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:19:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:19:09.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:19:10.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:19:10.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:19:11.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:19:11.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:19:12.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:19:13.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:19:13.593 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:19:14.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:19:14.723 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:19:14.724 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:19:14.724 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:19:14.724 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:19:14.731 | 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

2025-09-01 15:19:14.732 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:19:14.868 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:19:14.945 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch352
2025-09-01 15:19:18.155 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 352/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.8, lr: 7.353e-04, size: 512, ETA: 1:23:06
2025-09-01 15:19:21.251 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 352/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 7.346e-04, size: 256, ETA: 1:23:03
2025-09-01 15:19:24.338 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 352/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 1.1, lr: 7.338e-04, size: 448, ETA: 1:23:00
2025-09-01 15:19:27.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 352/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, 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: 7.330e-04, size: 384, ETA: 1:22:57
2025-09-01 15:19:30.429 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 352/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, 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: 7.322e-04, size: 448, ETA: 1:22:53
2025-09-01 15:19:33.393 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 352/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, 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: 7.314e-04, size: 480, ETA: 1:22:50
2025-09-01 15:19:34.806 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:19:40.999 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:19:41.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:19:41.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5917
2025-09-01 15:19:41.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4991
2025-09-01 15:19:42.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3671
2025-09-01 15:19:42.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4860
2025-09-01 15:19:42.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:19:42.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:19:42.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-09-01 15:19:42.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-09-01 15:19:42.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.367
2025-09-01 15:19:42.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.486
2025-09-01 15:19:42.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:19:42.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:19:42.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:19:42.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:19:42.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:19:42.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:19:42.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:19:42.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:19:42.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:19:42.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:19:42.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:19:43.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:19:43.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:19:44.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:19:44.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:19:45.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:19:45.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:19:46.190 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:19:46.190 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:19:46.190 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:19:46.191 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:19:46.202 | 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

2025-09-01 15:19:46.203 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:19:46.326 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:19:46.427 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch353
2025-09-01 15:19:49.286 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 353/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 7.303e-04, size: 512, ETA: 1:22:45
2025-09-01 15:19:52.281 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 353/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, 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: 7.295e-04, size: 448, ETA: 1:22:42
2025-09-01 15:19:55.380 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 353/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.006s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 7.287e-04, size: 544, ETA: 1:22:39
2025-09-01 15:19:58.372 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 353/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 7.280e-04, size: 256, ETA: 1:22:36
2025-09-01 15:20:01.356 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 353/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 9.9, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 4.6, cls_loss: 1.1, lr: 7.272e-04, size: 544, ETA: 1:22:33
2025-09-01 15:20:04.444 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 353/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 7.264e-04, size: 512, ETA: 1:22:30
2025-09-01 15:20:05.838 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:20:12.064 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:20:13.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:20:13.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5771
2025-09-01 15:20:13.941 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4893
2025-09-01 15:20:14.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3801
2025-09-01 15:20:14.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4822
2025-09-01 15:20:14.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:20:14.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:20:14.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.577
2025-09-01 15:20:14.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-09-01 15:20:14.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-09-01 15:20:14.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.482
2025-09-01 15:20:14.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:20:14.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:20:14.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:20:14.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:20:14.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:20:14.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:20:14.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:20:14.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:20:14.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:20:15.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:20:15.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:20:16.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:20:17.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:20:18.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:20:19.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:20:20.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:20:21.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:20:22.342 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:20:22.342 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 15:20:22.343 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:20:22.343 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:20:22.350 | 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

2025-09-01 15:20:22.351 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:20:22.439 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:20:22.531 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch354
2025-09-01 15:20:25.500 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 354/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, 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: 7.253e-04, size: 416, ETA: 1:22:25
2025-09-01 15:20:28.481 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 354/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, 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: 7.245e-04, size: 416, ETA: 1:22:22
2025-09-01 15:20:31.469 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 354/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.8, lr: 7.237e-04, size: 288, ETA: 1:22:18
2025-09-01 15:20:34.540 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 354/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.7, lr: 7.229e-04, size: 288, ETA: 1:22:15
2025-09-01 15:20:37.599 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 354/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 7.221e-04, size: 320, ETA: 1:22:12
2025-09-01 15:20:40.565 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 354/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 7.214e-04, size: 416, ETA: 1:22:09
2025-09-01 15:20:41.909 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:20:48.260 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:20:49.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:20:49.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6065
2025-09-01 15:20:49.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5070
2025-09-01 15:20:49.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3867
2025-09-01 15:20:49.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5001
2025-09-01 15:20:49.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:20:49.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:20:49.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-09-01 15:20:49.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-09-01 15:20:49.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-09-01 15:20:49.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-09-01 15:20:49.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:20:49.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:20:49.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:20:49.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:20:49.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:20:49.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:20:49.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:20:49.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:20:49.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:20:50.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:20:51.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:20:51.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:20:52.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:20:53.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:20:53.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:20:54.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:20:55.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:20:55.933 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:20:55.934 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:20:55.934 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:20:55.934 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:20:55.941 | 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: 7.13 ms

2025-09-01 15:20:55.943 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:20:56.022 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:20:56.107 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch355
2025-09-01 15:20:59.015 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 355/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 7.202e-04, size: 448, ETA: 1:22:04
2025-09-01 15:21:02.049 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 355/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, 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: 7.195e-04, size: 448, ETA: 1:22:01
2025-09-01 15:21:05.065 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 355/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 7.187e-04, size: 448, ETA: 1:21:58
2025-09-01 15:21:07.974 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 355/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 7.179e-04, size: 448, ETA: 1:21:55
2025-09-01 15:21:11.012 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 355/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.6, lr: 7.171e-04, size: 576, ETA: 1:21:51
2025-09-01 15:21:14.111 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 355/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, 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: 7.163e-04, size: 256, ETA: 1:21:48
2025-09-01 15:21:15.533 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:21:21.665 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:21:22.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:21:22.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5930
2025-09-01 15:21:23.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4956
2025-09-01 15:21:23.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3003
2025-09-01 15:21:23.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4629
2025-09-01 15:21:23.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:21:23.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:21:23.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-09-01 15:21:23.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.496
2025-09-01 15:21:23.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.300
2025-09-01 15:21:23.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.463
2025-09-01 15:21:23.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:21:23.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:21:23.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:21:23.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:21:23.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:21:23.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:21:23.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:21:23.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:21:23.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:21:23.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:21:24.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:21:25.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:21:25.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:21:26.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:21:26.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:21:27.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:21:28.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:21:28.642 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:21:28.643 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 15:21:28.643 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 15:21:28.643 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:21:28.651 | 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

2025-09-01 15:21:28.656 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:21:28.795 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:21:28.868 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch356
2025-09-01 15:21:31.740 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 356/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.8, lr: 7.152e-04, size: 320, ETA: 1:21:44
2025-09-01 15:21:34.850 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 356/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.7, lr: 7.144e-04, size: 448, ETA: 1:21:40
2025-09-01 15:21:37.893 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 356/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 7.137e-04, size: 512, ETA: 1:21:37
2025-09-01 15:21:40.899 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 356/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.7, lr: 7.129e-04, size: 480, ETA: 1:21:34
2025-09-01 15:21:43.985 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 356/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 7.121e-04, size: 512, ETA: 1:21:31
2025-09-01 15:21:46.983 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 356/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.7, lr: 7.113e-04, size: 448, ETA: 1:21:28
2025-09-01 15:21:48.370 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:21:54.698 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:21:55.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:21:55.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5955
2025-09-01 15:21:55.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5216
2025-09-01 15:21:55.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3850
2025-09-01 15:21:55.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5007
2025-09-01 15:21:55.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:21:55.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:21:55.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.596
2025-09-01 15:21:55.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-09-01 15:21:55.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-09-01 15:21:55.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-09-01 15:21:55.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:21:55.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:21:55.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:21:55.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:21:55.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:21:55.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:21:55.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:21:55.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:21:55.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:21:56.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:21:57.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:21:57.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:21:58.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:21:58.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:21:59.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:22:00.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:22:00.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:22:01.296 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:22:01.296 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:22:01.296 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:22:01.296 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:22:01.303 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.89 ms, Average inference time: 7.12 ms

2025-09-01 15:22:01.308 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:22:01.396 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:22:01.477 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch357
2025-09-01 15:22:04.392 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 357/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 7.102e-04, size: 288, ETA: 1:21:23
2025-09-01 15:22:07.384 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 357/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, 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: 7.094e-04, size: 512, ETA: 1:21:20
2025-09-01 15:22:10.576 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 357/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.5, lr: 7.086e-04, size: 416, ETA: 1:21:17
2025-09-01 15:22:13.637 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 357/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 7.079e-04, size: 544, ETA: 1:21:14
2025-09-01 15:22:16.618 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 357/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.6, lr: 7.071e-04, size: 544, ETA: 1:21:10
2025-09-01 15:22:19.619 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 357/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.5, lr: 7.063e-04, size: 448, ETA: 1:21:07
2025-09-01 15:22:20.995 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:22:27.076 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:22:27.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:22:27.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5761
2025-09-01 15:22:27.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4869
2025-09-01 15:22:27.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3722
2025-09-01 15:22:27.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4784
2025-09-01 15:22:27.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:22:27.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:22:27.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.576
2025-09-01 15:22:27.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.487
2025-09-01 15:22:27.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-09-01 15:22:27.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-09-01 15:22:27.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:22:27.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:22:27.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:22:27.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:22:27.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:22:27.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:22:27.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:22:27.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:22:27.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:22:28.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:22:28.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:22:29.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:22:29.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:22:29.853 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:22:30.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:22:30.582 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:22:30.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:22:31.307 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:22:31.307 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:22:31.307 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:22:31.307 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:22:31.314 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.85 ms, Average inference time: 7.17 ms

2025-09-01 15:22:31.315 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:22:31.405 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:22:31.488 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch358
2025-09-01 15:22:34.393 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 358/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 9.4, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 4.6, cls_loss: 0.7, lr: 7.052e-04, size: 576, ETA: 1:21:02
2025-09-01 15:22:37.593 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 358/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.157s, data_time: 0.006s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 7.044e-04, size: 288, ETA: 1:20:59
2025-09-01 15:22:40.539 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 358/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.7, lr: 7.036e-04, size: 448, ETA: 1:20:56
2025-09-01 15:22:43.565 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 358/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.7, cls_loss: 0.6, lr: 7.029e-04, size: 416, ETA: 1:20:53
2025-09-01 15:22:46.582 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 358/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 8.7, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.9, cls_loss: 0.7, lr: 7.021e-04, size: 448, ETA: 1:20:50
2025-09-01 15:22:49.598 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 358/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.6, cls_loss: 0.8, lr: 7.013e-04, size: 448, ETA: 1:20:47
2025-09-01 15:22:50.916 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:22:57.029 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:22:57.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:22:58.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5998
2025-09-01 15:22:58.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5137
2025-09-01 15:22:58.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3606
2025-09-01 15:22:58.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4914
2025-09-01 15:22:58.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:22:58.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:22:58.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-09-01 15:22:58.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.514
2025-09-01 15:22:58.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-09-01 15:22:58.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.491
2025-09-01 15:22:58.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:22:58.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:22:58.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:22:58.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:22:58.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:22:58.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:22:58.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:22:58.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:22:58.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:22:59.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:23:00.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:23:01.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:23:01.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:23:02.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:23:03.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:23:04.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:23:05.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:23:05.894 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:23:05.894 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:23:05.895 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:23:05.895 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:23:05.902 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.37 ms, Average NMS time: 0.92 ms, Average inference time: 7.29 ms

2025-09-01 15:23:05.903 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:23:05.991 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:23:06.075 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch359
2025-09-01 15:23:09.007 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 359/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 7.002e-04, size: 256, ETA: 1:20:42
2025-09-01 15:23:12.005 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 359/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.005s, total_loss: 8.6, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 3.8, cls_loss: 0.8, lr: 6.994e-04, size: 480, ETA: 1:20:39
2025-09-01 15:23:15.076 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 359/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 0.5, cls_loss: 0.5, lr: 6.986e-04, size: 416, ETA: 1:20:36
2025-09-01 15:23:18.075 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 359/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 6.979e-04, size: 416, ETA: 1:20:32
2025-09-01 15:23:21.045 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 359/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 6.971e-04, size: 576, ETA: 1:20:29
2025-09-01 15:23:24.170 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 359/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.8, lr: 6.963e-04, size: 448, ETA: 1:20:26
2025-09-01 15:23:25.544 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:23:31.749 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:23:32.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:23:33.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5785
2025-09-01 15:23:33.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5041
2025-09-01 15:23:33.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3568
2025-09-01 15:23:33.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4798
2025-09-01 15:23:33.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:23:33.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:23:33.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-09-01 15:23:33.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.504
2025-09-01 15:23:33.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.357
2025-09-01 15:23:33.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.480
2025-09-01 15:23:33.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:23:33.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:23:33.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:23:33.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:23:33.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:23:33.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:23:33.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:23:33.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:23:33.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:23:34.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:23:35.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:23:37.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:23:38.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:23:39.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:23:40.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:23:41.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:23:42.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:23:43.238 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:23:43.239 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:23:43.239 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:23:43.240 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:23:43.253 | 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

2025-09-01 15:23:43.254 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:23:43.378 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:23:43.484 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch360
2025-09-01 15:23:46.360 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 360/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 3.1, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 0.5, cls_loss: 0.5, lr: 6.952e-04, size: 256, ETA: 1:20:21
2025-09-01 15:23:49.391 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 360/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, 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: 6.944e-04, size: 320, ETA: 1:20:18
2025-09-01 15:23:52.447 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 360/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 1.1, lr: 6.936e-04, size: 256, ETA: 1:20:15
2025-09-01 15:23:55.465 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 360/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.8, iou_loss: 3.4, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 0.8, lr: 6.929e-04, size: 288, ETA: 1:20:12
2025-09-01 15:23:58.517 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 360/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.7, lr: 6.921e-04, size: 576, ETA: 1:20:09
2025-09-01 15:24:01.607 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 360/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.7, lr: 6.913e-04, size: 352, ETA: 1:20:05
2025-09-01 15:24:02.933 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:24:09.081 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:24:10.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:24:10.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5931
2025-09-01 15:24:11.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5234
2025-09-01 15:24:11.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3762
2025-09-01 15:24:11.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4976
2025-09-01 15:24:11.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:24:11.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:24:11.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-09-01 15:24:11.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-09-01 15:24:11.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.376
2025-09-01 15:24:11.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.498
2025-09-01 15:24:11.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:24:11.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:24:11.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:24:11.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:24:11.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:24:11.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:24:11.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:24:11.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:24:11.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:24:12.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:24:13.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:24:13.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:24:14.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:24:15.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:24:16.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:24:17.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:24:18.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:24:19.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:24:19.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:24:19.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:24:19.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:24:19.043 | 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: 7.17 ms

2025-09-01 15:24:19.045 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:24:19.125 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:24:19.218 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch361
2025-09-01 15:24:22.134 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 361/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.7, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.7, lr: 6.902e-04, size: 384, ETA: 1:20:01
2025-09-01 15:24:25.245 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 361/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.7, lr: 6.894e-04, size: 480, ETA: 1:19:58
2025-09-01 15:24:28.300 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 361/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.9, lr: 6.887e-04, size: 288, ETA: 1:19:54
2025-09-01 15:24:31.356 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 361/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 6.879e-04, size: 544, ETA: 1:19:51
2025-09-01 15:24:34.332 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 361/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 6.871e-04, size: 352, ETA: 1:19:48
2025-09-01 15:24:37.375 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 361/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 0.7, lr: 6.864e-04, size: 576, ETA: 1:19:45
2025-09-01 15:24:38.791 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:24:45.159 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:24:46.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:24:48.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5808
2025-09-01 15:24:48.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5016
2025-09-01 15:24:48.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3577
2025-09-01 15:24:48.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4800
2025-09-01 15:24:48.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:24:48.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:24:48.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.581
2025-09-01 15:24:48.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-09-01 15:24:48.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-09-01 15:24:48.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.480
2025-09-01 15:24:48.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:24:48.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:24:48.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:24:48.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:24:48.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:24:48.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:24:48.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:24:48.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:24:48.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:24:49.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:24:51.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:24:52.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:24:54.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:24:55.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:24:56.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:24:58.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:24:59.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:25:01.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:25:01.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 15:25:01.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:25:01.331 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:25:01.340 | 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

2025-09-01 15:25:01.346 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:25:01.428 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:25:01.510 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch362
2025-09-01 15:25:04.484 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 362/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 6.852e-04, size: 448, ETA: 1:19:40
2025-09-01 15:25:07.607 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 362/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, data_time: 0.005s, total_loss: 4.6, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.5, lr: 6.845e-04, size: 352, ETA: 1:19:37
2025-09-01 15:25:10.633 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 362/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 1.1, lr: 6.837e-04, size: 320, ETA: 1:19:34
2025-09-01 15:25:13.588 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 362/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 0.8, lr: 6.829e-04, size: 512, ETA: 1:19:31
2025-09-01 15:25:16.806 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 362/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.5, cls_loss: 0.8, lr: 6.822e-04, size: 384, ETA: 1:19:28
2025-09-01 15:25:19.909 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 362/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 9.0, iou_loss: 3.1, l1_loss: 1.4, conf_loss: 3.7, cls_loss: 0.8, lr: 6.814e-04, size: 448, ETA: 1:19:25
2025-09-01 15:25:21.351 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:25:27.481 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:25:28.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:25:28.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5735
2025-09-01 15:25:29.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5014
2025-09-01 15:25:29.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3325
2025-09-01 15:25:29.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4692
2025-09-01 15:25:29.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:25:29.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:25:29.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-09-01 15:25:29.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-09-01 15:25:29.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.333
2025-09-01 15:25:29.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.469
2025-09-01 15:25:29.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:25:29.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:25:29.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:25:29.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:25:29.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:25:29.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:25:29.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:25:29.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:25:29.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:25:29.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:25:30.688 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:25:31.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:25:32.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:25:33.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:25:33.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:25:34.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:25:35.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:25:36.210 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:25:36.211 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 15:25:36.211 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 15:25:36.211 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:25:36.218 | 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

2025-09-01 15:25:36.220 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:25:36.305 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:25:36.389 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch363
2025-09-01 15:25:39.304 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 363/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.9, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 6.803e-04, size: 288, ETA: 1:19:20
2025-09-01 15:25:42.241 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 363/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.145s, 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: 6.795e-04, size: 288, ETA: 1:19:17
2025-09-01 15:25:45.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 363/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, 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: 6.787e-04, size: 352, ETA: 1:19:14
2025-09-01 15:25:48.354 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 363/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 2.3, cls_loss: 0.8, lr: 6.780e-04, size: 544, ETA: 1:19:10
2025-09-01 15:25:51.437 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 363/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.9, lr: 6.772e-04, size: 384, ETA: 1:19:07
2025-09-01 15:25:54.425 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 363/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 10.0, iou_loss: 3.3, l1_loss: 1.2, conf_loss: 4.4, cls_loss: 1.1, lr: 6.764e-04, size: 384, ETA: 1:19:04
2025-09-01 15:25:55.726 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:26:02.007 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:26:03.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:26:03.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5899
2025-09-01 15:26:03.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5091
2025-09-01 15:26:04.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4046
2025-09-01 15:26:04.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5012
2025-09-01 15:26:04.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:26:04.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:26:04.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.590
2025-09-01 15:26:04.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-09-01 15:26:04.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-09-01 15:26:04.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-09-01 15:26:04.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:26:04.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:26:04.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:26:04.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:26:04.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:26:04.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:26:04.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:26:04.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:26:04.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:26:04.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:26:05.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:26:06.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:26:07.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:26:08.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:26:09.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:26:10.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:26:11.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:26:12.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:26:12.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:26:12.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:26:12.206 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:26:12.213 | 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

2025-09-01 15:26:12.214 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:26:12.301 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:26:12.384 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch364
2025-09-01 15:26:15.153 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 364/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.137s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.5, lr: 6.753e-04, size: 384, ETA: 1:18:59
2025-09-01 15:26:18.335 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 364/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 1.0, lr: 6.745e-04, size: 576, ETA: 1:18:56
2025-09-01 15:26:21.397 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 364/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 6.738e-04, size: 384, ETA: 1:18:53
2025-09-01 15:26:24.457 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 364/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.5, lr: 6.730e-04, size: 576, ETA: 1:18:50
2025-09-01 15:26:27.581 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 364/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.154s, 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: 6.722e-04, size: 320, ETA: 1:18:47
2025-09-01 15:26:30.635 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 364/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 6.715e-04, size: 480, ETA: 1:18:44
2025-09-01 15:26:32.004 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:26:38.179 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:26:38.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:26:38.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5493
2025-09-01 15:26:39.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4519
2025-09-01 15:26:39.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3361
2025-09-01 15:26:39.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4458
2025-09-01 15:26:39.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:26:39.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:26:39.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.549
2025-09-01 15:26:39.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.452
2025-09-01 15:26:39.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.336
2025-09-01 15:26:39.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.446
2025-09-01 15:26:39.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:26:39.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:26:39.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:26:39.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:26:39.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:26:39.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:26:39.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:26:39.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:26:39.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:26:39.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:26:39.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:26:40.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:26:40.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:26:41.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:26:41.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:26:41.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:26:42.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:26:42.639 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:26:42.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 15:26:42.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 15:26:42.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:26:42.646 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.84 ms, Average inference time: 7.12 ms

2025-09-01 15:26:42.648 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:26:42.733 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:26:42.816 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch365
2025-09-01 15:26:45.642 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 365/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.7, lr: 6.704e-04, size: 288, ETA: 1:18:39
2025-09-01 15:26:48.659 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 365/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.7, lr: 6.696e-04, size: 576, ETA: 1:18:36
2025-09-01 15:26:51.806 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 365/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, 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: 6.688e-04, size: 480, ETA: 1:18:33
2025-09-01 15:26:54.730 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 365/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 6.681e-04, size: 448, ETA: 1:18:29
2025-09-01 15:26:57.697 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 365/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, 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: 6.673e-04, size: 544, ETA: 1:18:26
2025-09-01 15:27:00.791 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 365/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 6.665e-04, size: 384, ETA: 1:18:23
2025-09-01 15:27:02.139 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:27:08.496 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:27:09.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:27:09.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5921
2025-09-01 15:27:09.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4900
2025-09-01 15:27:09.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3741
2025-09-01 15:27:09.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4854
2025-09-01 15:27:09.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:27:09.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:27:09.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-09-01 15:27:09.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.490
2025-09-01 15:27:09.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.374
2025-09-01 15:27:09.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.485
2025-09-01 15:27:09.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:27:09.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:27:09.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:27:09.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:27:09.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:27:09.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:27:09.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:27:09.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:27:09.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:27:10.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:27:11.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:27:11.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:27:12.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:27:13.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:27:13.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:27:14.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:27:15.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:27:15.864 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:27:15.864 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:27:15.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:27:15.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:27:15.872 | 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: 7.16 ms

2025-09-01 15:27:15.873 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:27:15.961 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:27:16.045 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch366
2025-09-01 15:27:18.889 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 366/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.5, lr: 6.654e-04, size: 416, ETA: 1:18:18
2025-09-01 15:27:21.975 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 366/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 6.647e-04, size: 448, ETA: 1:18:15
2025-09-01 15:27:24.974 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 366/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.5, lr: 6.639e-04, size: 256, ETA: 1:18:12
2025-09-01 15:27:27.997 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 366/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.5, lr: 6.631e-04, size: 480, ETA: 1:18:09
2025-09-01 15:27:31.035 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 366/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 6.624e-04, size: 576, ETA: 1:18:06
2025-09-01 15:27:34.222 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 366/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 6.616e-04, size: 256, ETA: 1:18:03
2025-09-01 15:27:35.500 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:27:41.785 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:27:42.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:27:43.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5955
2025-09-01 15:27:43.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5268
2025-09-01 15:27:43.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3776
2025-09-01 15:27:43.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5000
2025-09-01 15:27:43.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:27:43.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:27:43.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.595
2025-09-01 15:27:43.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-09-01 15:27:43.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-09-01 15:27:43.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-09-01 15:27:43.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:27:43.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:27:43.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:27:43.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:27:43.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:27:43.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:27:43.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:27:43.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:27:43.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:27:44.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:27:45.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:27:46.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:27:47.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:27:47.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:27:48.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:27:49.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:27:50.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:27:51.530 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:27:51.531 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:27:51.531 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:27:51.531 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:27:51.539 | 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

2025-09-01 15:27:51.539 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:27:51.672 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:27:51.746 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch367
2025-09-01 15:27:54.584 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 367/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.5, lr: 6.605e-04, size: 576, ETA: 1:17:58
2025-09-01 15:27:57.651 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 367/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 6.597e-04, size: 544, ETA: 1:17:55
2025-09-01 15:28:00.651 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 367/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.2, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.7, lr: 6.590e-04, size: 256, ETA: 1:17:51
2025-09-01 15:28:03.726 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 367/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.8, lr: 6.582e-04, size: 416, ETA: 1:17:48
2025-09-01 15:28:06.740 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 367/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 6.574e-04, size: 448, ETA: 1:17:45
2025-09-01 15:28:09.812 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 367/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.6, lr: 6.567e-04, size: 448, ETA: 1:17:42
2025-09-01 15:28:11.325 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:28:17.721 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:28:18.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:28:19.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5937
2025-09-01 15:28:19.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5197
2025-09-01 15:28:19.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3937
2025-09-01 15:28:19.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5023
2025-09-01 15:28:19.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:28:19.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:28:19.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.594
2025-09-01 15:28:19.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-09-01 15:28:19.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-09-01 15:28:19.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 15:28:19.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:28:19.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:28:19.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:28:19.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:28:19.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:28:19.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:28:19.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:28:19.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:28:19.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:28:20.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:28:20.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:28:21.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:28:22.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:28:23.145 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:28:23.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:28:24.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:28:25.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:28:26.097 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:28:26.097 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:28:26.098 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:28:26.098 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:28:26.105 | 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

2025-09-01 15:28:26.106 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:28:26.235 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:28:26.310 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch368
2025-09-01 15:28:29.197 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 368/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.6, lr: 6.556e-04, size: 416, ETA: 1:17:37
2025-09-01 15:28:32.310 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 368/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 6.548e-04, size: 256, ETA: 1:17:34
2025-09-01 15:28:35.294 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 368/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 6.541e-04, size: 544, ETA: 1:17:31
2025-09-01 15:28:38.379 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 368/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.8, lr: 6.533e-04, size: 256, ETA: 1:17:28
2025-09-01 15:28:41.460 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 368/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.8, lr: 6.525e-04, size: 448, ETA: 1:17:25
2025-09-01 15:28:44.527 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 368/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.7, lr: 6.518e-04, size: 288, ETA: 1:17:22
2025-09-01 15:28:45.793 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:28:51.987 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:28:53.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:28:53.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6067
2025-09-01 15:28:54.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5192
2025-09-01 15:28:54.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4008
2025-09-01 15:28:54.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5089
2025-09-01 15:28:54.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:28:54.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:28:54.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-09-01 15:28:54.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.519
2025-09-01 15:28:54.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-09-01 15:28:54.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.509
2025-09-01 15:28:54.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:28:54.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:28:54.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:28:54.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:28:54.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:28:54.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:28:54.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:28:54.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:28:54.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:28:55.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:28:56.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:28:57.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:28:58.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:28:59.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:28:59.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:29:00.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:29:01.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:29:02.893 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:29:02.894 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:29:02.894 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:29:02.894 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:29:02.901 | 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: 7.12 ms

2025-09-01 15:29:02.903 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:29:02.993 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:29:03.074 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch369
2025-09-01 15:29:06.043 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 369/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, 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: 6.507e-04, size: 256, ETA: 1:17:17
2025-09-01 15:29:08.937 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 369/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, 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: 6.499e-04, size: 512, ETA: 1:17:14
2025-09-01 15:29:11.915 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 369/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.5, lr: 6.491e-04, size: 288, ETA: 1:17:10
2025-09-01 15:29:14.872 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 369/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 6.484e-04, size: 544, ETA: 1:17:07
2025-09-01 15:29:17.841 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 369/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.4, conf_loss: 1.7, cls_loss: 0.6, lr: 6.476e-04, size: 448, ETA: 1:17:04
2025-09-01 15:29:20.940 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 369/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 1.1, lr: 6.469e-04, size: 512, ETA: 1:17:01
2025-09-01 15:29:22.296 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:29:28.419 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:29:29.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:29:29.765 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5891
2025-09-01 15:29:29.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5111
2025-09-01 15:29:30.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3791
2025-09-01 15:29:30.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4931
2025-09-01 15:29:30.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:29:30.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:29:30.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-09-01 15:29:30.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-09-01 15:29:30.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-09-01 15:29:30.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-09-01 15:29:30.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:29:30.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:29:30.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:29:30.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:29:30.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:29:30.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:29:30.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:29:30.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:29:30.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:29:30.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:29:31.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:29:32.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:29:32.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:29:33.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:29:34.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:29:35.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:29:35.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:29:36.461 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:29:36.462 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:29:36.462 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:29:36.462 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:29:36.469 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.89 ms, Average inference time: 7.08 ms

2025-09-01 15:29:36.470 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:29:36.558 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:29:36.639 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch370
2025-09-01 15:29:39.524 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 370/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 6.458e-04, size: 320, ETA: 1:16:56
2025-09-01 15:29:42.437 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 370/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 6.450e-04, size: 512, ETA: 1:16:53
2025-09-01 15:29:45.419 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 370/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.8, lr: 6.442e-04, size: 544, ETA: 1:16:50
2025-09-01 15:29:48.488 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 370/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.4, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 2.6, cls_loss: 0.5, lr: 6.435e-04, size: 576, ETA: 1:16:47
2025-09-01 15:29:51.630 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 370/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 6.427e-04, size: 352, ETA: 1:16:44
2025-09-01 15:29:54.619 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 370/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 6.420e-04, size: 544, ETA: 1:16:40
2025-09-01 15:29:56.003 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:30:02.204 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:30:02.939 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:30:03.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6140
2025-09-01 15:30:03.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5295
2025-09-01 15:30:03.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3852
2025-09-01 15:30:03.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5096
2025-09-01 15:30:03.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:30:03.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:30:03.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.614
2025-09-01 15:30:03.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-09-01 15:30:03.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-09-01 15:30:03.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.510
2025-09-01 15:30:03.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:30:03.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:30:03.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:30:03.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:30:03.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:30:03.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:30:03.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:30:03.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:30:03.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:30:04.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:30:04.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:30:05.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:30:06.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:30:06.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:30:07.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:30:08.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:30:08.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:30:09.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:30:09.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:30:09.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:30:09.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:30:09.257 | 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: 7.04 ms

2025-09-01 15:30:09.258 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:30:09.342 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:30:09.429 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch371
2025-09-01 15:30:12.406 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 371/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 9.1, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 3.8, cls_loss: 1.0, lr: 6.409e-04, size: 512, ETA: 1:16:36
2025-09-01 15:30:15.447 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 371/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 6.401e-04, size: 288, ETA: 1:16:33
2025-09-01 15:30:18.454 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 371/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 6.394e-04, size: 288, ETA: 1:16:29
2025-09-01 15:30:21.665 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 371/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 2.3, cls_loss: 0.6, lr: 6.386e-04, size: 352, ETA: 1:16:26
2025-09-01 15:30:24.779 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 371/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 6.378e-04, size: 576, ETA: 1:16:23
2025-09-01 15:30:27.729 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 371/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.4, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.9, lr: 6.371e-04, size: 256, ETA: 1:16:20
2025-09-01 15:30:29.185 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:30:35.391 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:30:35.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:30:36.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5504
2025-09-01 15:30:36.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5107
2025-09-01 15:30:36.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3047
2025-09-01 15:30:36.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4553
2025-09-01 15:30:36.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:30:36.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:30:36.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.550
2025-09-01 15:30:36.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-09-01 15:30:36.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.305
2025-09-01 15:30:36.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.455
2025-09-01 15:30:36.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:30:36.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:30:36.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:30:36.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:30:36.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:30:36.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:30:36.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:30:36.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:30:36.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:30:36.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:30:36.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:30:37.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:30:37.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:30:37.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:30:38.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:30:38.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:30:38.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:30:38.987 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:30:38.987 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 15:30:38.987 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 15:30:38.987 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:30:38.993 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.78 ms, Average inference time: 7.03 ms

2025-09-01 15:30:38.995 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:30:39.128 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:30:39.200 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch372
2025-09-01 15:30:42.205 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 372/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, 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: 6.360e-04, size: 576, ETA: 1:16:15
2025-09-01 15:30:45.300 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 372/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.6, lr: 6.352e-04, size: 480, ETA: 1:16:12
2025-09-01 15:30:48.317 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 372/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 6.345e-04, size: 320, ETA: 1:16:09
2025-09-01 15:30:51.321 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 372/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 2.1, cls_loss: 0.7, lr: 6.337e-04, size: 544, ETA: 1:16:06
2025-09-01 15:30:54.498 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 372/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 6.330e-04, size: 416, ETA: 1:16:03
2025-09-01 15:30:57.459 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 372/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 6.322e-04, size: 544, ETA: 1:16:00
2025-09-01 15:30:58.826 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:31:04.991 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:31:05.714 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:31:06.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5995
2025-09-01 15:31:06.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5232
2025-09-01 15:31:06.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3932
2025-09-01 15:31:06.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5053
2025-09-01 15:31:06.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:31:06.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:31:06.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-09-01 15:31:06.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-09-01 15:31:06.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-09-01 15:31:06.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-09-01 15:31:06.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:31:06.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:31:06.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:31:06.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:31:06.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:31:06.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:31:06.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:31:06.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:31:06.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:31:06.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:31:07.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:31:08.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:31:08.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:31:09.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:31:10.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:31:10.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:31:11.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:31:11.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:31:11.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:31:11.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:31:11.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:31:12.005 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.86 ms, Average inference time: 7.07 ms

2025-09-01 15:31:12.006 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:31:12.089 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:31:12.171 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch373
2025-09-01 15:31:15.042 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 373/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.6, lr: 6.311e-04, size: 320, ETA: 1:15:55
2025-09-01 15:31:18.058 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 373/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 6.304e-04, size: 448, ETA: 1:15:52
2025-09-01 15:31:21.045 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 373/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.2, l1_loss: 0.3, conf_loss: 2.3, cls_loss: 0.4, lr: 6.296e-04, size: 384, ETA: 1:15:49
2025-09-01 15:31:23.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 373/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.144s, 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: 6.289e-04, size: 416, ETA: 1:15:45
2025-09-01 15:31:27.024 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 373/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 9.8, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 5.1, cls_loss: 0.8, lr: 6.281e-04, size: 256, ETA: 1:15:42
2025-09-01 15:31:29.985 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 373/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 6.274e-04, size: 480, ETA: 1:15:39
2025-09-01 15:31:31.326 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:31:37.641 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:31:38.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:31:39.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5874
2025-09-01 15:31:39.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5264
2025-09-01 15:31:39.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3812
2025-09-01 15:31:39.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4984
2025-09-01 15:31:39.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:31:39.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:31:39.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.587
2025-09-01 15:31:39.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-09-01 15:31:39.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-09-01 15:31:39.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.498
2025-09-01 15:31:39.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:31:39.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:31:39.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:31:39.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:31:39.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:31:39.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:31:39.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:31:39.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:31:39.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:31:40.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:31:40.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:31:41.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:31:42.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:31:43.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:31:43.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:31:44.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:31:45.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:31:46.113 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:31:46.114 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 15:31:46.114 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:31:46.114 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:31:46.121 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.41 ms, Average NMS time: 0.94 ms, Average inference time: 7.35 ms

2025-09-01 15:31:46.122 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:31:46.211 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:31:46.292 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch374
2025-09-01 15:31:49.133 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 374/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 6.263e-04, size: 320, ETA: 1:15:34
2025-09-01 15:31:52.127 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 374/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, 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: 6.255e-04, size: 352, ETA: 1:15:31
2025-09-01 15:31:55.111 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 374/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.7, lr: 6.248e-04, size: 384, ETA: 1:15:28
2025-09-01 15:31:58.112 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 374/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.5, lr: 6.240e-04, size: 448, ETA: 1:15:25
2025-09-01 15:32:01.142 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 374/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.1, cls_loss: 0.7, lr: 6.232e-04, size: 320, ETA: 1:15:22
2025-09-01 15:32:04.212 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 374/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.8, lr: 6.225e-04, size: 576, ETA: 1:15:18
2025-09-01 15:32:05.639 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:32:11.863 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:32:12.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:32:12.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5751
2025-09-01 15:32:12.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5160
2025-09-01 15:32:12.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3486
2025-09-01 15:32:12.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4799
2025-09-01 15:32:12.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:32:12.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:32:12.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.575
2025-09-01 15:32:12.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-09-01 15:32:12.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.349
2025-09-01 15:32:12.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.480
2025-09-01 15:32:12.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:32:12.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:32:12.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:32:12.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:32:12.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:32:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:32:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:32:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:32:12.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:32:13.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:32:13.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:32:14.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:32:14.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:32:15.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:32:15.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:32:16.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:32:16.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:32:17.049 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:32:17.050 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:32:17.050 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:32:17.051 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:32:17.063 | 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

2025-09-01 15:32:17.063 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:32:17.183 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:32:17.283 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch375
2025-09-01 15:32:20.263 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 375/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.6, lr: 6.214e-04, size: 384, ETA: 1:15:14
2025-09-01 15:32:23.297 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 375/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 1.0, lr: 6.207e-04, size: 288, ETA: 1:15:11
2025-09-01 15:32:26.316 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 375/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.8, lr: 6.199e-04, size: 480, ETA: 1:15:07
2025-09-01 15:32:29.394 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 375/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 6.192e-04, size: 352, ETA: 1:15:04
2025-09-01 15:32:32.351 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 375/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.7, cls_loss: 0.8, lr: 6.184e-04, size: 544, ETA: 1:15:01
2025-09-01 15:32:35.408 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 375/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 6.177e-04, size: 256, ETA: 1:14:58
2025-09-01 15:32:36.783 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:32:42.950 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:32:44.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:32:44.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5948
2025-09-01 15:32:44.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5062
2025-09-01 15:32:44.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3870
2025-09-01 15:32:44.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4960
2025-09-01 15:32:44.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:32:44.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:32:44.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.595
2025-09-01 15:32:44.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-09-01 15:32:44.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-09-01 15:32:44.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.496
2025-09-01 15:32:44.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:32:44.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:32:44.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:32:44.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:32:44.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:32:44.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:32:44.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:32:44.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:32:44.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:32:45.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:32:46.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:32:47.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:32:48.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:32:49.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:32:50.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:32:51.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:32:52.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:32:53.441 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:32:53.441 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:32:53.441 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:32:53.441 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:32:53.448 | 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

2025-09-01 15:32:53.449 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:32:53.537 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:32:53.619 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch376
2025-09-01 15:32:56.565 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 376/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, 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: 6.166e-04, size: 256, ETA: 1:14:53
2025-09-01 15:32:59.516 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 376/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 6.158e-04, size: 352, ETA: 1:14:50
2025-09-01 15:33:02.501 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 376/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.5, lr: 6.151e-04, size: 384, ETA: 1:14:47
2025-09-01 15:33:05.535 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 376/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.5, cls_loss: 0.6, lr: 6.143e-04, size: 544, ETA: 1:14:44
2025-09-01 15:33:08.466 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 376/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, 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: 6.136e-04, size: 256, ETA: 1:14:41
2025-09-01 15:33:11.504 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 376/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, 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: 6.128e-04, size: 544, ETA: 1:14:37
2025-09-01 15:33:12.920 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:33:19.154 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:33:20.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:33:20.591 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6097
2025-09-01 15:33:20.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5252
2025-09-01 15:33:20.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4218
2025-09-01 15:33:20.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5189
2025-09-01 15:33:20.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:33:20.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:33:20.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.610
2025-09-01 15:33:20.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-09-01 15:33:20.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.422
2025-09-01 15:33:20.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.519
2025-09-01 15:33:20.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:33:20.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:33:20.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:33:20.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:33:20.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:33:20.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:33:20.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:33:20.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:33:20.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:33:21.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:33:22.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:33:23.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:33:23.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:33:24.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:33:25.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:33:25.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:33:26.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:33:27.474 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:33:27.474 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:33:27.474 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 15:33:27.475 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:33:27.487 | 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: 7.20 ms

2025-09-01 15:33:27.493 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:33:27.612 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:33:27.711 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch377
2025-09-01 15:33:30.705 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 377/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 6.117e-04, size: 384, ETA: 1:14:33
2025-09-01 15:33:33.703 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 377/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.5, lr: 6.110e-04, size: 448, ETA: 1:14:30
2025-09-01 15:33:36.756 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 377/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 3.1, cls_loss: 0.7, lr: 6.102e-04, size: 544, ETA: 1:14:27
2025-09-01 15:33:39.829 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 377/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.4Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 3.0, cls_loss: 0.7, lr: 6.095e-04, size: 576, ETA: 1:14:23
2025-09-01 15:33:43.002 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 377/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, 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: 6.087e-04, size: 288, ETA: 1:14:20
2025-09-01 15:33:46.050 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 377/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.9, iou_loss: 2.6, l1_loss: 1.7, conf_loss: 3.9, cls_loss: 0.7, lr: 6.080e-04, size: 544, ETA: 1:14:17
2025-09-01 15:33:47.409 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:33:53.502 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:33:54.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:33:55.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5840
2025-09-01 15:33:55.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5147
2025-09-01 15:33:55.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3629
2025-09-01 15:33:55.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4872
2025-09-01 15:33:55.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:33:55.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:33:55.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-09-01 15:33:55.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 15:33:55.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-09-01 15:33:55.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-09-01 15:33:55.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:33:55.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:33:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:33:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:33:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:33:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:33:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:33:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:33:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:33:56.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:33:57.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:33:58.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:33:58.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:33:59.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:34:00.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:34:01.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:34:02.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:34:03.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:34:03.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:34:03.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:34:03.199 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:34:03.207 | 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

2025-09-01 15:34:03.208 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:34:03.290 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:34:03.371 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch378
2025-09-01 15:34:06.197 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 378/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 7.9, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 3.3, cls_loss: 0.7, lr: 6.069e-04, size: 544, ETA: 1:14:12
2025-09-01 15:34:09.198 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 378/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 6.062e-04, size: 320, ETA: 1:14:09
2025-09-01 15:34:12.218 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 378/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 6.054e-04, size: 352, ETA: 1:14:06
2025-09-01 15:34:15.129 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 378/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 6.047e-04, size: 384, ETA: 1:14:03
2025-09-01 15:34:18.142 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 378/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 6.039e-04, size: 576, ETA: 1:14:00
2025-09-01 15:34:21.232 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 378/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.6, lr: 6.032e-04, size: 480, ETA: 1:13:57
2025-09-01 15:34:22.609 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:34:28.888 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:34:29.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:34:30.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6018
2025-09-01 15:34:30.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5114
2025-09-01 15:34:30.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3845
2025-09-01 15:34:30.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4992
2025-09-01 15:34:30.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:34:30.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:34:30.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.602
2025-09-01 15:34:30.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-09-01 15:34:30.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-09-01 15:34:30.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.499
2025-09-01 15:34:30.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:34:30.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:34:30.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:34:30.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:34:30.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:34:30.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:34:30.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:34:30.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:34:30.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:34:31.498 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:34:32.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:34:33.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:34:33.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:34:34.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:34:35.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:34:36.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:34:37.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:34:37.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:34:37.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:34:37.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:34:37.885 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:34:37.892 | 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

2025-09-01 15:34:37.895 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:34:38.014 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:34:38.091 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch379
2025-09-01 15:34:41.024 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 379/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.7, cls_loss: 0.5, lr: 6.021e-04, size: 544, ETA: 1:13:52
2025-09-01 15:34:44.116 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 379/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.5, lr: 6.014e-04, size: 576, ETA: 1:13:49
2025-09-01 15:34:47.301 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 379/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.6, lr: 6.006e-04, size: 544, ETA: 1:13:46
2025-09-01 15:34:50.380 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 379/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 5.999e-04, size: 416, ETA: 1:13:43
2025-09-01 15:34:53.400 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 379/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.3, iou_loss: 1.6, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 5.991e-04, size: 576, ETA: 1:13:39
2025-09-01 15:34:56.502 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 379/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 3.5, cls_loss: 0.9, lr: 5.984e-04, size: 416, ETA: 1:13:36
2025-09-01 15:34:57.878 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:35:04.205 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:35:04.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:35:05.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5892
2025-09-01 15:35:05.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5226
2025-09-01 15:35:05.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3766
2025-09-01 15:35:05.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4961
2025-09-01 15:35:05.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:35:05.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:35:05.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-09-01 15:35:05.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-09-01 15:35:05.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-09-01 15:35:05.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.496
2025-09-01 15:35:05.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:35:05.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:35:05.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:35:05.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:35:05.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:35:05.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:35:05.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:35:05.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:35:05.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:35:06.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:35:06.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:35:07.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:35:08.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:35:08.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:35:09.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:35:09.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:35:10.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:35:11.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:35:11.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:35:11.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:35:11.166 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:35:11.173 | 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

2025-09-01 15:35:11.175 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:35:11.256 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:35:11.339 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch380
2025-09-01 15:35:14.461 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 380/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, 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: 5.973e-04, size: 256, ETA: 1:13:32
2025-09-01 15:35:17.489 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 380/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 5.966e-04, size: 320, ETA: 1:13:29
2025-09-01 15:35:20.416 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 380/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.2, iou_loss: 1.7, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.5, lr: 5.958e-04, size: 512, ETA: 1:13:25
2025-09-01 15:35:23.451 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 380/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.8, lr: 5.951e-04, size: 416, ETA: 1:13:22
2025-09-01 15:35:26.483 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 380/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 5.943e-04, size: 288, ETA: 1:13:19
2025-09-01 15:35:29.522 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 380/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.8, cls_loss: 0.6, lr: 5.936e-04, size: 416, ETA: 1:13:16
2025-09-01 15:35:30.809 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:35:37.009 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:35:37.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:35:38.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6106
2025-09-01 15:35:38.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5238
2025-09-01 15:35:38.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4145
2025-09-01 15:35:38.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5163
2025-09-01 15:35:38.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.611
2025-09-01 15:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-09-01 15:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-09-01 15:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.516
2025-09-01 15:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:35:38.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:35:38.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:35:39.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:35:40.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:35:40.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:35:41.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:35:42.302 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:35:43.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:35:43.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:35:44.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:35:45.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:35:45.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 15:35:45.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 15:35:45.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:35:45.179 | 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: 7.09 ms

2025-09-01 15:35:45.180 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:35:45.266 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:35:45.347 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch381
2025-09-01 15:35:48.246 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 381/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 5.925e-04, size: 352, ETA: 1:13:11
2025-09-01 15:35:51.396 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 381/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 5.918e-04, size: 480, ETA: 1:13:08
2025-09-01 15:35:54.532 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 381/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 9.3, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 4.2, cls_loss: 0.8, lr: 5.910e-04, size: 576, ETA: 1:13:05
2025-09-01 15:35:57.493 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 381/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.8, lr: 5.903e-04, size: 416, ETA: 1:13:02
2025-09-01 15:36:00.483 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 381/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 3.1, iou_loss: 1.3, l1_loss: 0.4, conf_loss: 1.0, cls_loss: 0.4, lr: 5.896e-04, size: 352, ETA: 1:12:59
2025-09-01 15:36:03.417 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 381/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, 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: 5.888e-04, size: 320, ETA: 1:12:55
2025-09-01 15:36:04.742 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:36:11.086 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:36:12.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:36:12.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6086
2025-09-01 15:36:12.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4992
2025-09-01 15:36:12.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3719
2025-09-01 15:36:12.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4932
2025-09-01 15:36:12.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:36:12.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:36:12.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-09-01 15:36:12.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.499
2025-09-01 15:36:12.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-09-01 15:36:12.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.493
2025-09-01 15:36:12.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:36:12.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:36:12.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:36:12.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:36:12.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:36:12.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:36:12.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:36:12.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:36:12.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:36:13.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:36:14.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:36:15.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:36:16.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:36:16.927 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:36:17.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:36:18.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:36:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:36:20.263 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:36:20.263 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:36:20.263 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:36:20.263 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:36:20.271 | 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

2025-09-01 15:36:20.272 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:36:20.391 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:36:20.467 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch382
2025-09-01 15:36:23.312 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 382/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 5.877e-04, size: 448, ETA: 1:12:51
2025-09-01 15:36:26.391 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 382/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.6, lr: 5.870e-04, size: 544, ETA: 1:12:48
2025-09-01 15:36:29.538 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 382/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, 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: 5.863e-04, size: 448, ETA: 1:12:45
2025-09-01 15:36:32.742 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 382/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.6, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.9, cls_loss: 1.2, lr: 5.855e-04, size: 544, ETA: 1:12:41
2025-09-01 15:36:35.752 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 382/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.7, lr: 5.848e-04, size: 256, ETA: 1:12:38
2025-09-01 15:36:38.800 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 382/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, 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: 5.841e-04, size: 384, ETA: 1:12:35
2025-09-01 15:36:40.140 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:36:46.548 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:36:47.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:36:48.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6081
2025-09-01 15:36:48.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5293
2025-09-01 15:36:48.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3687
2025-09-01 15:36:48.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5020
2025-09-01 15:36:48.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:36:48.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:36:48.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-09-01 15:36:48.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-09-01 15:36:48.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-09-01 15:36:48.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 15:36:48.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:36:48.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:36:48.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:36:48.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:36:48.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:36:48.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:36:48.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:36:48.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:36:48.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:36:49.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:36:49.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:36:50.578 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:36:51.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:36:52.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:36:53.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:36:53.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:36:54.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:36:55.295 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:36:55.296 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:36:55.296 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:36:55.296 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:36:55.303 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.93 ms, Average inference time: 7.19 ms

2025-09-01 15:36:55.305 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:36:55.453 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:36:55.530 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch383
2025-09-01 15:36:58.473 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 383/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 14.3, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 9.4, cls_loss: 2.0, lr: 5.830e-04, size: 416, ETA: 1:12:31
2025-09-01 15:37:01.528 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 383/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, 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: 5.822e-04, size: 352, ETA: 1:12:27
2025-09-01 15:37:04.626 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 383/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 3.4, cls_loss: 0.7, lr: 5.815e-04, size: 544, ETA: 1:12:24
2025-09-01 15:37:07.635 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 383/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 5.808e-04, size: 512, ETA: 1:12:21
2025-09-01 15:37:10.676 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 383/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 5.800e-04, size: 352, ETA: 1:12:18
2025-09-01 15:37:13.823 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 383/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.2, iou_loss: 1.9, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.5, lr: 5.793e-04, size: 576, ETA: 1:12:15
2025-09-01 15:37:15.239 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:37:21.484 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:37:22.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:37:22.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5408
2025-09-01 15:37:22.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4932
2025-09-01 15:37:22.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3128
2025-09-01 15:37:22.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4489
2025-09-01 15:37:22.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:37:22.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:37:22.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-09-01 15:37:22.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.493
2025-09-01 15:37:22.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.313
2025-09-01 15:37:22.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.449
2025-09-01 15:37:22.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:37:22.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:37:22.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:37:22.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:37:22.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:37:22.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:37:22.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:37:22.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:37:22.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:37:22.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:37:23.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:37:23.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:37:24.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:37:24.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:37:25.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:37:25.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:37:26.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:37:26.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:37:26.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 15:37:26.513 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.45
2025-09-01 15:37:26.514 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:37:26.526 | 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: 7.21 ms

2025-09-01 15:37:26.527 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:37:26.644 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:37:26.718 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch384
2025-09-01 15:37:29.625 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 384/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 5.782e-04, size: 256, ETA: 1:12:10
2025-09-01 15:37:32.770 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 384/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 5.775e-04, size: 352, ETA: 1:12:07
2025-09-01 15:37:35.716 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 384/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.6, lr: 5.768e-04, size: 448, ETA: 1:12:04
2025-09-01 15:37:38.614 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 384/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.143s, 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: 5.760e-04, size: 416, ETA: 1:12:01
2025-09-01 15:37:41.563 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 384/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.6, lr: 5.753e-04, size: 352, ETA: 1:11:58
2025-09-01 15:37:44.519 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 384/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.8, lr: 5.746e-04, size: 256, ETA: 1:11:54
2025-09-01 15:37:45.897 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:37:52.067 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:37:53.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:37:53.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5961
2025-09-01 15:37:53.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5293
2025-09-01 15:37:53.856 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3812
2025-09-01 15:37:53.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5022
2025-09-01 15:37:53.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:37:53.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:37:53.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.596
2025-09-01 15:37:53.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-09-01 15:37:53.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-09-01 15:37:53.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 15:37:53.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:37:53.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:37:53.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:37:53.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:37:53.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:37:53.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:37:53.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:37:53.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:37:53.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:37:54.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:37:55.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:37:56.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:37:57.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:37:58.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:37:59.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:37:59.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:38:00.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:38:01.569 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:38:01.569 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:38:01.569 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:38:01.569 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:38:01.577 | 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

2025-09-01 15:38:01.578 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:38:01.660 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:38:01.742 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch385
2025-09-01 15:38:04.605 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 385/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 5.735e-04, size: 512, ETA: 1:11:50
2025-09-01 15:38:07.541 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 385/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 5.728e-04, size: 288, ETA: 1:11:46
2025-09-01 15:38:10.459 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 385/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 3.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 0.6, cls_loss: 0.5, lr: 5.720e-04, size: 384, ETA: 1:11:43
2025-09-01 15:38:13.548 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 385/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 5.713e-04, size: 256, ETA: 1:11:40
2025-09-01 15:38:16.528 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 385/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 17.9, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 17.9, cls_loss: 0.0, lr: 5.706e-04, size: 320, ETA: 1:11:37
2025-09-01 15:38:19.487 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 385/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 5.698e-04, size: 320, ETA: 1:11:34
2025-09-01 15:38:20.821 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:38:27.118 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:38:28.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:38:28.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5920
2025-09-01 15:38:28.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5121
2025-09-01 15:38:29.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4125
2025-09-01 15:38:29.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5056
2025-09-01 15:38:29.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:38:29.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:38:29.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.592
2025-09-01 15:38:29.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-09-01 15:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-09-01 15:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.506
2025-09-01 15:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:38:29.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:38:29.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:38:29.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:38:30.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:38:31.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:38:32.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:38:33.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:38:34.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:38:35.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:38:35.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:38:36.862 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:38:36.862 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:38:36.863 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:38:36.863 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:38:36.870 | 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: 7.13 ms

2025-09-01 15:38:36.871 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:38:36.951 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:38:37.034 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch386
2025-09-01 15:38:39.922 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 386/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 5.688e-04, size: 352, ETA: 1:11:29
2025-09-01 15:38:42.986 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 386/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.5, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.5, lr: 5.680e-04, size: 448, ETA: 1:11:26
2025-09-01 15:38:46.013 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 386/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 5.673e-04, size: 384, ETA: 1:11:23
2025-09-01 15:38:49.042 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 386/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 9.2, iou_loss: 3.4, l1_loss: 1.6, conf_loss: 3.2, cls_loss: 1.0, lr: 5.666e-04, size: 544, ETA: 1:11:20
2025-09-01 15:38:52.184 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 386/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 5.658e-04, size: 576, ETA: 1:11:16
2025-09-01 15:38:55.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 386/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.7, conf_loss: 2.4, cls_loss: 0.8, lr: 5.651e-04, size: 576, ETA: 1:11:13
2025-09-01 15:38:56.704 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:39:02.992 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:39:03.732 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:39:04.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5969
2025-09-01 15:39:04.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5215
2025-09-01 15:39:04.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3736
2025-09-01 15:39:04.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4973
2025-09-01 15:39:04.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:39:04.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:39:04.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.597
2025-09-01 15:39:04.366 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-09-01 15:39:04.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.374
2025-09-01 15:39:04.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-09-01 15:39:04.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:39:04.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:39:04.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:39:04.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:39:04.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:39:04.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:39:04.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:39:04.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:39:04.368 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:39:04.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:39:05.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:39:06.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:39:06.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:39:07.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:39:08.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:39:08.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:39:09.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:39:10.025 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:39:10.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:39:10.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:39:10.026 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:39:10.033 | 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

2025-09-01 15:39:10.034 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:39:10.164 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:39:10.239 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch387
2025-09-01 15:39:13.249 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 387/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 5.640e-04, size: 512, ETA: 1:11:09
2025-09-01 15:39:16.393 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 387/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.7, lr: 5.633e-04, size: 448, ETA: 1:11:06
2025-09-01 15:39:19.413 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 387/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 5.626e-04, size: 384, ETA: 1:11:03
2025-09-01 15:39:22.448 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 387/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.3, l1_loss: 0.5, conf_loss: 2.0, cls_loss: 0.4, lr: 5.618e-04, size: 512, ETA: 1:10:59
2025-09-01 15:39:25.506 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 387/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, 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: 5.611e-04, size: 448, ETA: 1:10:56
2025-09-01 15:39:28.511 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 387/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.7, lr: 5.604e-04, size: 320, ETA: 1:10:53
2025-09-01 15:39:29.850 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:39:36.212 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:39:37.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:39:37.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5957
2025-09-01 15:39:38.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5294
2025-09-01 15:39:38.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3951
2025-09-01 15:39:38.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5068
2025-09-01 15:39:38.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:39:38.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:39:38.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.596
2025-09-01 15:39:38.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-09-01 15:39:38.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-09-01 15:39:38.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.507
2025-09-01 15:39:38.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:39:38.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:39:38.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:39:38.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:39:38.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:39:38.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:39:38.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:39:38.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:39:38.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:39:38.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:39:39.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:39:40.584 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:39:41.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:39:42.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:39:43.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:39:43.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:39:44.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:39:45.598 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:39:45.598 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:39:45.598 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:39:45.598 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:39:45.605 | 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

2025-09-01 15:39:45.606 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:39:45.728 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:39:45.805 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch388
2025-09-01 15:39:48.709 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 388/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.7, lr: 5.593e-04, size: 384, ETA: 1:10:48
2025-09-01 15:39:51.784 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 388/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.6, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 5.586e-04, size: 448, ETA: 1:10:45
2025-09-01 15:39:54.764 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 388/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, 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: 5.579e-04, size: 448, ETA: 1:10:42
2025-09-01 15:39:57.751 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 388/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 5.571e-04, size: 480, ETA: 1:10:39
2025-09-01 15:40:00.734 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 388/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.7, cls_loss: 0.7, lr: 5.564e-04, size: 448, ETA: 1:10:36
2025-09-01 15:40:03.831 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 388/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.7, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.5, lr: 5.557e-04, size: 512, ETA: 1:10:33
2025-09-01 15:40:05.269 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:40:11.479 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:40:12.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:40:12.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6009
2025-09-01 15:40:12.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5369
2025-09-01 15:40:12.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3650
2025-09-01 15:40:12.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5009
2025-09-01 15:40:12.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:40:12.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:40:12.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.601
2025-09-01 15:40:12.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-09-01 15:40:12.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-09-01 15:40:12.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-09-01 15:40:12.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:40:12.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:40:12.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:40:12.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:40:12.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:40:12.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:40:12.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:40:12.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:40:12.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:40:13.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:40:14.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:40:14.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:40:15.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:40:16.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:40:16.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:40:17.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:40:17.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:40:18.521 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:40:18.522 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 15:40:18.522 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:40:18.522 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:40:18.529 | 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

2025-09-01 15:40:18.530 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:40:18.688 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:40:18.760 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch389
2025-09-01 15:40:21.695 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 389/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 5.546e-04, size: 320, ETA: 1:10:28
2025-09-01 15:40:24.716 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 389/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.8, lr: 5.539e-04, size: 384, ETA: 1:10:25
2025-09-01 15:40:27.679 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 389/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, 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: 5.532e-04, size: 320, ETA: 1:10:22
2025-09-01 15:40:30.783 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 389/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.8, lr: 5.525e-04, size: 288, ETA: 1:10:19
2025-09-01 15:40:33.791 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 389/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 5.517e-04, size: 288, ETA: 1:10:15
2025-09-01 15:40:36.821 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 389/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.8, lr: 5.510e-04, size: 288, ETA: 1:10:12
2025-09-01 15:40:38.107 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:40:44.452 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:40:45.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:40:45.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6012
2025-09-01 15:40:45.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5151
2025-09-01 15:40:45.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4027
2025-09-01 15:40:45.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5063
2025-09-01 15:40:45.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:40:45.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:40:45.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.601
2025-09-01 15:40:45.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 15:40:45.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-09-01 15:40:45.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.506
2025-09-01 15:40:45.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:40:45.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:40:45.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:40:45.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:40:45.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:40:45.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:40:45.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:40:45.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:40:45.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:40:46.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:40:47.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:40:47.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:40:48.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:40:48.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:40:49.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:40:49.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:40:50.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:40:51.156 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:40:51.157 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:40:51.157 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:40:51.157 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:40:51.164 | 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: 7.18 ms

2025-09-01 15:40:51.165 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:40:51.253 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:40:51.336 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch390
2025-09-01 15:40:54.246 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 390/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 5.500e-04, size: 448, ETA: 1:10:08
2025-09-01 15:40:57.257 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 390/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.5, lr: 5.492e-04, size: 480, ETA: 1:10:04
2025-09-01 15:41:00.234 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 390/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 1.6, conf_loss: 2.1, cls_loss: 0.7, lr: 5.485e-04, size: 352, ETA: 1:10:01
2025-09-01 15:41:03.314 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 390/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 16.6, iou_loss: 3.3, l1_loss: 1.5, conf_loss: 10.5, cls_loss: 1.3, lr: 5.478e-04, size: 480, ETA: 1:09:58
2025-09-01 15:41:06.363 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 390/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, 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: 5.471e-04, size: 448, ETA: 1:09:55
2025-09-01 15:41:09.343 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 390/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 8.7, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 3.6, cls_loss: 0.8, lr: 5.463e-04, size: 512, ETA: 1:09:52
2025-09-01 15:41:10.789 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:41:17.127 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:41:17.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:41:18.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6086
2025-09-01 15:41:18.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5371
2025-09-01 15:41:18.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3957
2025-09-01 15:41:18.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5138
2025-09-01 15:41:18.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:41:18.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:41:18.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-09-01 15:41:18.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-09-01 15:41:18.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-09-01 15:41:18.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-09-01 15:41:18.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:41:18.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:41:18.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:41:18.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:41:18.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:41:18.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:41:18.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:41:18.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:41:18.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:41:19.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:41:19.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:41:20.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:41:20.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:41:21.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:41:22.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:41:22.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:41:23.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:41:24.017 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:41:24.017 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:41:24.017 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:41:24.017 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:41:24.024 | 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: 6.96 ms

2025-09-01 15:41:24.025 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:41:24.112 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:41:24.199 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch391
2025-09-01 15:41:27.048 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 391/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 1.1, lr: 5.453e-04, size: 288, ETA: 1:09:47
2025-09-01 15:41:30.108 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 391/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.6, lr: 5.446e-04, size: 576, ETA: 1:09:44
2025-09-01 15:41:33.075 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 391/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.9, lr: 5.438e-04, size: 288, ETA: 1:09:41
2025-09-01 15:41:36.065 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 391/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 5.431e-04, size: 384, ETA: 1:09:38
2025-09-01 15:41:39.068 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 391/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 0.7, conf_loss: 3.4, cls_loss: 0.8, lr: 5.424e-04, size: 416, ETA: 1:09:35
2025-09-01 15:41:42.175 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 391/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.3, l1_loss: 1.4, conf_loss: 3.1, cls_loss: 0.7, lr: 5.417e-04, size: 576, ETA: 1:09:31
2025-09-01 15:41:43.587 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:41:49.803 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:41:50.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:41:51.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6027
2025-09-01 15:41:51.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5154
2025-09-01 15:41:51.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3678
2025-09-01 15:41:51.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4953
2025-09-01 15:41:51.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:41:51.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:41:51.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.603
2025-09-01 15:41:51.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 15:41:51.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-09-01 15:41:51.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-09-01 15:41:51.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:41:51.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:41:51.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:41:51.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:41:51.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:41:51.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:41:51.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:41:51.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:41:51.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:41:52.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:41:53.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:41:54.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:41:55.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:41:56.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:41:57.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:41:57.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:41:58.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:41:59.758 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:41:59.758 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:41:59.759 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:41:59.759 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:41:59.766 | 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

2025-09-01 15:41:59.767 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:41:59.930 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:42:00.003 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch392
2025-09-01 15:42:02.850 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 392/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.141s, data_time: 0.003s, total_loss: 7.0, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 5.406e-04, size: 320, ETA: 1:09:27
2025-09-01 15:42:05.862 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 392/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 5.399e-04, size: 448, ETA: 1:09:24
2025-09-01 15:42:08.860 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 392/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 3.3, cls_loss: 1.2, lr: 5.392e-04, size: 544, ETA: 1:09:21
2025-09-01 15:42:11.877 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 392/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 5.385e-04, size: 384, ETA: 1:09:17
2025-09-01 15:42:14.887 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 392/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 5.377e-04, size: 288, ETA: 1:09:14
2025-09-01 15:42:17.850 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 392/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.6, lr: 5.370e-04, size: 256, ETA: 1:09:11
2025-09-01 15:42:19.323 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:42:25.524 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:42:26.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:42:26.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5977
2025-09-01 15:42:26.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5146
2025-09-01 15:42:27.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3803
2025-09-01 15:42:27.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4975
2025-09-01 15:42:27.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:42:27.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:42:27.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-09-01 15:42:27.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 15:42:27.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-09-01 15:42:27.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.498
2025-09-01 15:42:27.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:42:27.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:42:27.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:42:27.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:42:27.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:42:27.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:42:27.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:42:27.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:42:27.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:42:27.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:42:28.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:42:29.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:42:29.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:42:30.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:42:31.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:42:31.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:42:32.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:42:33.292 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:42:33.292 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:42:33.292 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:42:33.292 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:42:33.299 | 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

2025-09-01 15:42:33.300 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:42:33.390 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:42:33.472 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch393
2025-09-01 15:42:36.500 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 393/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.2, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.6, lr: 5.360e-04, size: 320, ETA: 1:09:07
2025-09-01 15:42:39.574 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 393/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.6, l1_loss: 1.7, conf_loss: 3.5, cls_loss: 0.7, lr: 5.353e-04, size: 544, ETA: 1:09:03
2025-09-01 15:42:42.656 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 393/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, 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: 0.7, lr: 5.345e-04, size: 352, ETA: 1:09:00
2025-09-01 15:42:45.642 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 393/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 5.338e-04, size: 512, ETA: 1:08:57
2025-09-01 15:42:48.582 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 393/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.8, lr: 5.331e-04, size: 256, ETA: 1:08:54
2025-09-01 15:42:51.622 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 393/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.5, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.5, lr: 5.324e-04, size: 448, ETA: 1:08:51
2025-09-01 15:42:52.924 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:42:59.139 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:43:00.181 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:43:00.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6048
2025-09-01 15:43:01.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5048
2025-09-01 15:43:01.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3864
2025-09-01 15:43:01.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4987
2025-09-01 15:43:01.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:43:01.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:43:01.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.605
2025-09-01 15:43:01.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-09-01 15:43:01.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.386
2025-09-01 15:43:01.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.499
2025-09-01 15:43:01.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:43:01.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:43:01.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:43:01.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:43:01.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:43:01.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:43:01.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:43:01.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:43:01.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:43:02.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:43:03.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:43:03.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:43:04.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:43:05.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:43:06.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:43:07.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:43:08.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:43:09.525 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:43:09.526 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:43:09.526 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:43:09.526 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:43:09.533 | 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

2025-09-01 15:43:09.535 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:43:09.625 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:43:09.705 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch394
2025-09-01 15:43:12.584 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 394/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.143s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 5.314e-04, size: 320, ETA: 1:08:46
2025-09-01 15:43:15.653 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 394/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, 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: 5.306e-04, size: 544, ETA: 1:08:43
2025-09-01 15:43:18.716 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 394/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 5.299e-04, size: 320, ETA: 1:08:40
2025-09-01 15:43:21.805 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 394/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 5.292e-04, size: 416, ETA: 1:08:37
2025-09-01 15:43:24.902 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 394/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.8, lr: 5.285e-04, size: 512, ETA: 1:08:34
2025-09-01 15:43:28.098 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 394/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 2.5, cls_loss: 0.7, lr: 5.278e-04, size: 544, ETA: 1:08:31
2025-09-01 15:43:29.487 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:43:35.624 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:43:36.300 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:43:36.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5984
2025-09-01 15:43:36.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5248
2025-09-01 15:43:36.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3589
2025-09-01 15:43:36.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4940
2025-09-01 15:43:36.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:43:36.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:43:36.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-09-01 15:43:36.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-09-01 15:43:36.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.359
2025-09-01 15:43:36.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-09-01 15:43:36.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:43:36.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:43:36.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:43:36.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:43:36.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:43:36.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:43:36.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:43:36.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:43:36.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:43:37.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:43:38.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:43:38.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:43:39.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:43:39.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:43:40.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:43:41.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:43:41.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:43:42.148 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:43:42.148 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:43:42.149 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:43:42.149 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:43:42.156 | 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

2025-09-01 15:43:42.157 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:43:42.239 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:43:42.323 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch395
2025-09-01 15:43:45.200 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 395/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, 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: 5.267e-04, size: 320, ETA: 1:08:26
2025-09-01 15:43:48.208 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 395/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 5.260e-04, size: 448, ETA: 1:08:23
2025-09-01 15:43:51.331 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 395/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 5.253e-04, size: 416, ETA: 1:08:20
2025-09-01 15:43:54.285 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 395/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 5.246e-04, size: 416, ETA: 1:08:17
2025-09-01 15:43:57.227 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 395/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.7, lr: 5.239e-04, size: 256, ETA: 1:08:13
2025-09-01 15:44:00.259 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 395/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 5.232e-04, size: 384, ETA: 1:08:10
2025-09-01 15:44:01.666 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:44:07.783 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:44:08.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:44:08.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6205
2025-09-01 15:44:09.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5179
2025-09-01 15:44:09.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4013
2025-09-01 15:44:09.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5132
2025-09-01 15:44:09.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:44:09.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:44:09.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.620
2025-09-01 15:44:09.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.518
2025-09-01 15:44:09.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-09-01 15:44:09.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-09-01 15:44:09.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:44:09.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:44:09.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:44:09.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:44:09.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:44:09.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:44:09.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:44:09.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:44:09.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:44:09.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:44:10.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:44:10.931 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:44:11.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:44:12.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:44:12.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:44:13.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:44:13.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:44:14.435 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:44:14.435 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:44:14.435 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:44:14.435 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:44:14.442 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.18 ms, Average NMS time: 0.89 ms, Average inference time: 7.07 ms

2025-09-01 15:44:14.443 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:44:14.531 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:44:14.613 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch396
2025-09-01 15:44:17.539 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 396/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.005s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 5.221e-04, size: 352, ETA: 1:08:06
2025-09-01 15:44:20.488 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 396/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 5.214e-04, size: 512, ETA: 1:08:02
2025-09-01 15:44:23.548 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 396/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 5.207e-04, size: 384, ETA: 1:07:59
2025-09-01 15:44:26.587 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 396/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 2.5, cls_loss: 0.6, lr: 5.200e-04, size: 448, ETA: 1:07:56
2025-09-01 15:44:29.594 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 396/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 5.193e-04, size: 256, ETA: 1:07:53
2025-09-01 15:44:32.682 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 396/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 1.8, cls_loss: 0.7, lr: 5.186e-04, size: 416, ETA: 1:07:50
2025-09-01 15:44:33.964 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:44:40.196 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:44:41.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:44:41.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6093
2025-09-01 15:44:42.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5359
2025-09-01 15:44:42.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3788
2025-09-01 15:44:42.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5080
2025-09-01 15:44:42.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:44:42.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:44:42.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-09-01 15:44:42.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-09-01 15:44:42.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-09-01 15:44:42.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.508
2025-09-01 15:44:42.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:44:42.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:44:42.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:44:42.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:44:42.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:44:42.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:44:42.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:44:42.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:44:42.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:44:42.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:44:43.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:44:44.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:44:45.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:44:46.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:44:47.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:44:48.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:44:48.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:44:49.717 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:44:49.717 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:44:49.718 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:44:49.718 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:44:49.725 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.95 ms, Average inference time: 7.26 ms

2025-09-01 15:44:49.726 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:44:49.813 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:44:49.895 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch397
2025-09-01 15:44:52.793 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 397/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, 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: 5.175e-04, size: 480, ETA: 1:07:45
2025-09-01 15:44:55.851 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 397/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 2.9, iou_loss: 1.0, l1_loss: 0.4, conf_loss: 1.1, cls_loss: 0.4, lr: 5.168e-04, size: 320, ETA: 1:07:42
2025-09-01 15:44:58.838 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 397/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 5.161e-04, size: 256, ETA: 1:07:39
2025-09-01 15:45:01.891 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 397/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 1.6, conf_loss: 2.6, cls_loss: 0.7, lr: 5.154e-04, size: 576, ETA: 1:07:36
2025-09-01 15:45:05.058 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 397/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 5.147e-04, size: 512, ETA: 1:07:33
2025-09-01 15:45:08.057 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 397/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 5.140e-04, size: 288, ETA: 1:07:30
2025-09-01 15:45:09.358 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:45:15.532 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:45:16.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:45:17.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6043
2025-09-01 15:45:17.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5243
2025-09-01 15:45:17.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3740
2025-09-01 15:45:17.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5009
2025-09-01 15:45:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:45:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:45:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-09-01 15:45:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-09-01 15:45:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.374
2025-09-01 15:45:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-09-01 15:45:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:45:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:45:17.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:45:17.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:45:17.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:45:17.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:45:17.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:45:17.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:45:17.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:45:19.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:45:20.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:45:21.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:45:22.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:45:23.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:45:24.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:45:25.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:45:27.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:45:28.193 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:45:28.194 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:45:28.194 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:45:28.194 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:45:28.201 | 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: 7.18 ms

2025-09-01 15:45:28.202 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:45:28.341 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:45:28.416 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch398
2025-09-01 15:45:31.373 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 398/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.6, cls_loss: 0.7, lr: 5.130e-04, size: 448, ETA: 1:07:25
2025-09-01 15:45:34.474 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 398/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.9, l1_loss: 1.4, conf_loss: 1.7, cls_loss: 0.7, lr: 5.122e-04, size: 480, ETA: 1:07:22
2025-09-01 15:45:37.675 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 398/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.158s, 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: 5.115e-04, size: 256, ETA: 1:07:19
2025-09-01 15:45:40.585 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 398/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 5.108e-04, size: 480, ETA: 1:07:16
2025-09-01 15:45:43.686 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 398/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 5.101e-04, size: 416, ETA: 1:07:12
2025-09-01 15:45:46.662 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 398/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 8.7, iou_loss: 2.9, l1_loss: 1.6, conf_loss: 3.5, cls_loss: 0.7, lr: 5.094e-04, size: 448, ETA: 1:07:09
2025-09-01 15:45:48.056 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:45:54.140 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:45:55.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:45:55.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6121
2025-09-01 15:45:55.684 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5244
2025-09-01 15:45:55.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4053
2025-09-01 15:45:55.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5139
2025-09-01 15:45:55.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:45:55.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:45:55.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.612
2025-09-01 15:45:55.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-09-01 15:45:55.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-09-01 15:45:55.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-09-01 15:45:55.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:45:55.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:45:55.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:45:55.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:45:55.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:45:55.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:45:55.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:45:55.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:45:55.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:45:56.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:45:57.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:45:57.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:45:58.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:45:59.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:46:00.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:46:00.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:46:01.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:46:02.262 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:46:02.262 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:46:02.262 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:46:02.262 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:46:02.270 | 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

2025-09-01 15:46:02.270 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:46:02.357 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:46:02.440 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch399
2025-09-01 15:46:05.386 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 399/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.003s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 5.084e-04, size: 288, ETA: 1:07:05
2025-09-01 15:46:08.372 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 399/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 1.0, lr: 5.077e-04, size: 576, ETA: 1:07:01
2025-09-01 15:46:11.419 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 399/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 5.070e-04, size: 384, ETA: 1:06:58
2025-09-01 15:46:14.557 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 399/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.5, lr: 5.063e-04, size: 480, ETA: 1:06:55
2025-09-01 15:46:17.565 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 399/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, 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: 5.056e-04, size: 320, ETA: 1:06:52
2025-09-01 15:46:20.627 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 399/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 5.049e-04, size: 576, ETA: 1:06:49
2025-09-01 15:46:22.022 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:46:28.137 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:46:28.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:46:29.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5941
2025-09-01 15:46:29.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5015
2025-09-01 15:46:29.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3399
2025-09-01 15:46:29.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4785
2025-09-01 15:46:29.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:46:29.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:46:29.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.594
2025-09-01 15:46:29.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-09-01 15:46:29.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.340
2025-09-01 15:46:29.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-09-01 15:46:29.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:46:29.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:46:29.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:46:29.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:46:29.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:46:29.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:46:29.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:46:29.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:46:29.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:46:30.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:46:30.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:46:31.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:46:31.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:46:32.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:46:32.954 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:46:33.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:46:34.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:46:34.637 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:46:34.637 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:46:34.638 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:46:34.638 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:46:34.645 | 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

2025-09-01 15:46:34.646 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:46:34.735 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:46:34.858 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch400
2025-09-01 15:46:37.714 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 400/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.8, lr: 5.038e-04, size: 256, ETA: 1:06:44
2025-09-01 15:46:40.659 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 400/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 5.031e-04, size: 448, ETA: 1:06:41
2025-09-01 15:46:43.714 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 400/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.7, lr: 5.024e-04, size: 576, ETA: 1:06:38
2025-09-01 15:46:46.798 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 400/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, 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: 5.017e-04, size: 384, ETA: 1:06:35
2025-09-01 15:46:49.798 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 400/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, 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: 5.010e-04, size: 448, ETA: 1:06:32
2025-09-01 15:46:52.865 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 400/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 9.6, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 6.0, cls_loss: 0.6, lr: 5.003e-04, size: 512, ETA: 1:06:29
2025-09-01 15:46:54.281 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:47:00.521 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:47:01.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:47:01.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5980
2025-09-01 15:47:01.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5150
2025-09-01 15:47:02.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3922
2025-09-01 15:47:02.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5017
2025-09-01 15:47:02.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:47:02.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:47:02.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-09-01 15:47:02.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 15:47:02.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-09-01 15:47:02.078 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 15:47:02.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:47:02.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:47:02.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:47:02.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:47:02.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:47:02.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:47:02.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:47:02.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:47:02.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:47:02.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:47:03.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:47:04.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:47:04.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:47:05.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:47:06.287 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:47:06.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:47:07.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:47:08.302 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:47:08.302 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:47:08.302 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:47:08.302 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:47:08.309 | 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

2025-09-01 15:47:08.313 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:47:08.417 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:47:08.505 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch401
2025-09-01 15:47:11.433 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 401/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.5, lr: 4.993e-04, size: 320, ETA: 1:06:24
2025-09-01 15:47:14.413 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 401/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 4.986e-04, size: 320, ETA: 1:06:21
2025-09-01 15:47:17.601 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 401/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.8, lr: 4.979e-04, size: 544, ETA: 1:06:18
2025-09-01 15:47:20.608 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 401/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 4.972e-04, size: 256, ETA: 1:06:15
2025-09-01 15:47:23.595 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 401/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 4.965e-04, size: 480, ETA: 1:06:12
2025-09-01 15:47:26.618 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 401/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, 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: 4.958e-04, size: 576, ETA: 1:06:08
2025-09-01 15:47:28.030 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:47:34.150 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:47:34.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:47:35.354 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5982
2025-09-01 15:47:35.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5211
2025-09-01 15:47:35.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3703
2025-09-01 15:47:35.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4966
2025-09-01 15:47:35.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:47:35.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:47:35.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-09-01 15:47:35.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 15:47:35.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-09-01 15:47:35.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-09-01 15:47:35.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:47:35.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:47:35.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:47:35.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:47:35.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:47:35.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:47:35.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:47:35.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:47:35.548 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:47:36.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:47:36.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:47:37.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:47:38.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:47:38.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:47:39.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:47:39.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:47:40.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:47:41.242 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:47:41.242 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:47:41.243 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:47:41.243 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:47:41.250 | 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: 7.05 ms

2025-09-01 15:47:41.251 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:47:41.330 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:47:41.416 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch402
2025-09-01 15:47:44.251 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 402/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 4.948e-04, size: 352, ETA: 1:06:04
2025-09-01 15:47:47.222 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 402/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 4.941e-04, size: 320, ETA: 1:06:01
2025-09-01 15:47:50.224 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 402/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 4.934e-04, size: 352, ETA: 1:05:57
2025-09-01 15:47:53.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 402/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.5, lr: 4.927e-04, size: 352, ETA: 1:05:54
2025-09-01 15:47:56.317 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 402/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.7, lr: 4.920e-04, size: 352, ETA: 1:05:51
2025-09-01 15:47:59.365 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 402/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 9.0, iou_loss: 3.0, l1_loss: 1.5, conf_loss: 3.6, cls_loss: 0.9, lr: 4.913e-04, size: 480, ETA: 1:05:48
2025-09-01 15:48:00.740 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:48:07.149 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:48:07.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:48:08.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5911
2025-09-01 15:48:08.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5154
2025-09-01 15:48:08.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3705
2025-09-01 15:48:08.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4923
2025-09-01 15:48:08.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:48:08.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:48:08.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.591
2025-09-01 15:48:08.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 15:48:08.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-09-01 15:48:08.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.492
2025-09-01 15:48:08.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:48:08.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:48:08.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:48:08.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:48:08.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:48:08.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:48:08.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:48:08.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:48:08.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:48:09.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:48:10.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:48:10.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:48:11.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:48:12.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:48:12.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:48:13.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:48:13.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:48:14.594 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:48:14.594 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:48:14.594 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:48:14.594 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:48:14.601 | 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

2025-09-01 15:48:14.602 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:48:14.697 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:48:14.780 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch403
2025-09-01 15:48:17.745 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 403/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 4.903e-04, size: 544, ETA: 1:05:43
2025-09-01 15:48:20.744 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 403/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 4.896e-04, size: 512, ETA: 1:05:40
2025-09-01 15:48:23.801 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 403/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, 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: 4.889e-04, size: 416, ETA: 1:05:37
2025-09-01 15:48:26.949 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 403/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.2, cls_loss: 0.6, lr: 4.882e-04, size: 576, ETA: 1:05:34
2025-09-01 15:48:29.955 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 403/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, 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: 4.875e-04, size: 256, ETA: 1:05:31
2025-09-01 15:48:33.052 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 403/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.9, lr: 4.868e-04, size: 256, ETA: 1:05:28
2025-09-01 15:48:34.419 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:48:40.765 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:48:42.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:48:43.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5951
2025-09-01 15:48:43.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5112
2025-09-01 15:48:43.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3798
2025-09-01 15:48:43.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4954
2025-09-01 15:48:43.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:48:43.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:48:43.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.595
2025-09-01 15:48:43.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-09-01 15:48:43.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-09-01 15:48:43.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.495
2025-09-01 15:48:43.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:48:43.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:48:43.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:48:43.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:48:43.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:48:43.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:48:43.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:48:43.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:48:43.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:48:45.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:48:46.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:48:48.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:48:49.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:48:50.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:48:52.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:48:53.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:48:55.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:48:56.544 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:48:56.544 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:48:56.544 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:48:56.545 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:48:56.555 | 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

2025-09-01 15:48:56.556 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:48:56.640 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:48:56.733 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch404
2025-09-01 15:48:59.865 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 404/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.7, lr: 4.858e-04, size: 576, ETA: 1:05:23
2025-09-01 15:49:03.145 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 404/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.162s, data_time: 0.004s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.6, lr: 4.851e-04, size: 448, ETA: 1:05:20
2025-09-01 15:49:06.166 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 404/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 1.0, lr: 4.844e-04, size: 384, ETA: 1:05:17
2025-09-01 15:49:09.213 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 404/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.7, lr: 4.837e-04, size: 384, ETA: 1:05:14
2025-09-01 15:49:12.196 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 404/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.005s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 4.830e-04, size: 288, ETA: 1:05:11
2025-09-01 15:49:15.252 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 404/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, 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: 4.823e-04, size: 352, ETA: 1:05:08
2025-09-01 15:49:16.690 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:49:22.964 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:49:23.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:49:24.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6005
2025-09-01 15:49:24.294 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5151
2025-09-01 15:49:24.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3843
2025-09-01 15:49:24.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5000
2025-09-01 15:49:24.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:49:24.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:49:24.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-09-01 15:49:24.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 15:49:24.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-09-01 15:49:24.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-09-01 15:49:24.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:49:24.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:49:24.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:49:24.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:49:24.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:49:24.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:49:24.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:49:24.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:49:24.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:49:24.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:49:25.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:49:26.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:49:26.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:49:27.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:49:28.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:49:28.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:49:29.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:49:30.020 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:49:30.020 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:49:30.020 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:49:30.020 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:49:30.027 | 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

2025-09-01 15:49:30.029 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:49:30.111 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:49:30.194 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch405
2025-09-01 15:49:33.059 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 405/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.143s, 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: 4.813e-04, size: 320, ETA: 1:05:03
2025-09-01 15:49:35.969 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 405/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, 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: 4.806e-04, size: 288, ETA: 1:05:00
2025-09-01 15:49:39.036 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 405/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, 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: 4.799e-04, size: 480, ETA: 1:04:57
2025-09-01 15:49:42.109 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 405/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 4.792e-04, size: 448, ETA: 1:04:54
2025-09-01 15:49:45.206 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 405/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 3.9, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.6, lr: 4.785e-04, size: 320, ETA: 1:04:51
2025-09-01 15:49:48.190 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 405/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, 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: 4.778e-04, size: 544, ETA: 1:04:47
2025-09-01 15:49:49.546 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:49:55.789 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:49:56.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:49:57.044 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6022
2025-09-01 15:49:57.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5136
2025-09-01 15:49:57.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3572
2025-09-01 15:49:57.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4910
2025-09-01 15:49:57.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:49:57.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:49:57.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.602
2025-09-01 15:49:57.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.514
2025-09-01 15:49:57.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.357
2025-09-01 15:49:57.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.491
2025-09-01 15:49:57.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:49:57.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:49:57.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:49:57.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:49:57.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:49:57.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:49:57.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:49:57.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:49:57.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:49:57.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:49:58.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:49:59.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:49:59.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:50:00.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:50:01.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:50:02.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:50:02.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:50:03.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:50:03.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:50:03.366 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 15:50:03.367 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:50:03.374 | 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

2025-09-01 15:50:03.375 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:50:03.462 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:50:03.543 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch406
2025-09-01 15:50:06.485 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 406/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.005s, total_loss: 8.5, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 3.9, cls_loss: 1.0, lr: 4.768e-04, size: 512, ETA: 1:04:43
2025-09-01 15:50:09.497 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 406/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 1.4, cls_loss: 0.5, lr: 4.761e-04, size: 256, ETA: 1:04:40
2025-09-01 15:50:12.447 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 406/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 4.754e-04, size: 352, ETA: 1:04:37
2025-09-01 15:50:15.391 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 406/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.9, cls_loss: 0.6, lr: 4.747e-04, size: 352, ETA: 1:04:33
2025-09-01 15:50:18.483 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 406/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 4.740e-04, size: 416, ETA: 1:04:30
2025-09-01 15:50:21.601 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 406/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 4.734e-04, size: 448, ETA: 1:04:27
2025-09-01 15:50:22.993 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:50:29.185 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:50:30.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:50:30.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6106
2025-09-01 15:50:30.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5312
2025-09-01 15:50:31.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3975
2025-09-01 15:50:31.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5131
2025-09-01 15:50:31.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:50:31.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:50:31.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.611
2025-09-01 15:50:31.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-09-01 15:50:31.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-09-01 15:50:31.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-09-01 15:50:31.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:50:31.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:50:31.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:50:31.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:50:31.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:50:31.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:50:31.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:50:31.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:50:31.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:50:31.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:50:32.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:50:33.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:50:34.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:50:35.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:50:35.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:50:36.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:50:37.428 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:50:38.254 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:50:38.254 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:50:38.255 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:50:38.255 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:50:38.262 | 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

2025-09-01 15:50:38.263 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:50:38.344 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:50:38.428 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch407
2025-09-01 15:50:41.279 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 407/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 1.1, cls_loss: 0.5, lr: 4.724e-04, size: 320, ETA: 1:04:23
2025-09-01 15:50:44.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 407/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, 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: 4.717e-04, size: 320, ETA: 1:04:19
2025-09-01 15:50:47.504 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 407/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.159s, 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: 4.710e-04, size: 416, ETA: 1:04:16
2025-09-01 15:50:50.457 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 407/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 4.703e-04, size: 320, ETA: 1:04:13
2025-09-01 15:50:53.439 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 407/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 4.696e-04, size: 576, ETA: 1:04:10
2025-09-01 15:50:56.465 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 407/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 1.1, lr: 4.689e-04, size: 448, ETA: 1:04:07
2025-09-01 15:50:57.803 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:51:04.023 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:51:04.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:51:05.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5988
2025-09-01 15:51:05.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5215
2025-09-01 15:51:05.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3903
2025-09-01 15:51:05.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5035
2025-09-01 15:51:05.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:51:05.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:51:05.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-09-01 15:51:05.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-09-01 15:51:05.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-09-01 15:51:05.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-09-01 15:51:05.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:51:05.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:51:05.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:51:05.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:51:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:51:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:51:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:51:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:51:05.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:51:06.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:51:07.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:51:08.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:51:08.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:51:09.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:51:10.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:51:11.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:51:11.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:51:12.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:51:12.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:51:12.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:51:12.603 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:51:12.610 | 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

2025-09-01 15:51:12.612 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:51:12.694 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:51:12.776 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch408
2025-09-01 15:51:15.669 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 408/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, data_time: 0.005s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 4.679e-04, size: 448, ETA: 1:04:02
2025-09-01 15:51:18.559 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 408/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 10.3, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 10.3, cls_loss: 0.0, lr: 4.672e-04, size: 448, ETA: 1:03:59
2025-09-01 15:51:21.697 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 408/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, 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: 4.665e-04, size: 448, ETA: 1:03:56
2025-09-01 15:51:24.787 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 408/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.6, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 4.659e-04, size: 448, ETA: 1:03:53
2025-09-01 15:51:27.854 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 408/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 4.652e-04, size: 480, ETA: 1:03:50
2025-09-01 15:51:30.860 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 408/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.8, lr: 4.645e-04, size: 288, ETA: 1:03:47
2025-09-01 15:51:32.210 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:51:38.431 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:51:39.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:51:39.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6040
2025-09-01 15:51:39.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5212
2025-09-01 15:51:39.915 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3786
2025-09-01 15:51:39.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5013
2025-09-01 15:51:39.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:51:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:51:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-09-01 15:51:39.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 15:51:39.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-09-01 15:51:39.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-09-01 15:51:39.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:51:39.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:51:39.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:51:39.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:51:39.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:51:39.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:51:39.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:51:39.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:51:39.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:51:40.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:51:41.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:51:42.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:51:42.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:51:43.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:51:44.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:51:44.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:51:45.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:51:46.021 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:51:46.021 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:51:46.022 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:51:46.022 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:51:46.029 | 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: 7.20 ms

2025-09-01 15:51:46.031 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:51:46.167 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:51:46.241 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch409
2025-09-01 15:51:49.153 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 409/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.003s, total_loss: 8.4, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 3.5, cls_loss: 0.8, lr: 4.635e-04, size: 544, ETA: 1:03:42
2025-09-01 15:51:52.158 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 409/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.1, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 4.628e-04, size: 320, ETA: 1:03:39
2025-09-01 15:51:55.164 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 409/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 2.9, iou_loss: 1.0, l1_loss: 0.3, conf_loss: 1.3, cls_loss: 0.4, lr: 4.621e-04, size: 288, ETA: 1:03:36
2025-09-01 15:51:58.134 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 409/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.1, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.8, lr: 4.614e-04, size: 576, ETA: 1:03:33
2025-09-01 15:52:01.245 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 409/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, 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: 4.608e-04, size: 256, ETA: 1:03:29
2025-09-01 15:52:04.114 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 409/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 4.601e-04, size: 384, ETA: 1:03:26
2025-09-01 15:52:05.471 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:52:11.657 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:52:12.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:52:13.172 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5908
2025-09-01 15:52:13.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5230
2025-09-01 15:52:13.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3910
2025-09-01 15:52:13.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5016
2025-09-01 15:52:13.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:52:13.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:52:13.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.591
2025-09-01 15:52:13.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-09-01 15:52:13.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-09-01 15:52:13.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 15:52:13.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:52:13.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:52:13.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:52:13.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:52:13.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:52:13.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:52:13.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:52:13.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:52:13.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:52:14.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:52:14.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:52:15.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:52:16.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:52:17.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:52:17.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:52:18.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:52:19.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:52:20.139 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:52:20.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 15:52:20.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:52:20.140 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:52:20.147 | 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

2025-09-01 15:52:20.148 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:52:20.230 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:52:20.318 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch410
2025-09-01 15:52:23.259 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 410/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 2.0, cls_loss: 0.6, lr: 4.591e-04, size: 576, ETA: 1:03:22
2025-09-01 15:52:26.376 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 410/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 4.584e-04, size: 288, ETA: 1:03:19
2025-09-01 15:52:29.428 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 410/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, 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: 4.577e-04, size: 480, ETA: 1:03:15
2025-09-01 15:52:32.438 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 410/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 4.570e-04, size: 352, ETA: 1:03:12
2025-09-01 15:52:35.511 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 410/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, 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: 4.563e-04, size: 384, ETA: 1:03:09
2025-09-01 15:52:38.609 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 410/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.8, lr: 4.557e-04, size: 320, ETA: 1:03:06
2025-09-01 15:52:39.935 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:52:46.219 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:52:47.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:52:47.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6034
2025-09-01 15:52:47.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5247
2025-09-01 15:52:47.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3887
2025-09-01 15:52:47.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5056
2025-09-01 15:52:47.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:52:47.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:52:47.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.603
2025-09-01 15:52:47.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-09-01 15:52:47.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-09-01 15:52:47.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.506
2025-09-01 15:52:47.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:52:47.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:52:47.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:52:47.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:52:47.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:52:47.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:52:47.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:52:47.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:52:47.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:52:48.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:52:49.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:52:50.155 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:52:50.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:52:51.620 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:52:52.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:52:53.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:52:53.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:52:54.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:52:54.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:52:54.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:52:54.641 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:52:54.654 | 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: 7.14 ms

2025-09-01 15:52:54.655 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:52:54.776 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:52:54.882 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch411
2025-09-01 15:52:57.834 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 411/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 4.547e-04, size: 480, ETA: 1:03:01
2025-09-01 15:53:00.785 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 411/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.8, lr: 4.540e-04, size: 320, ETA: 1:02:58
2025-09-01 15:53:03.787 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 411/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.9, cls_loss: 0.5, lr: 4.533e-04, size: 544, ETA: 1:02:55
2025-09-01 15:53:06.702 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 411/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 0.6, cls_loss: 0.5, lr: 4.526e-04, size: 384, ETA: 1:02:52
2025-09-01 15:53:09.631 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 411/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, 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: 4.520e-04, size: 256, ETA: 1:02:49
2025-09-01 15:53:12.697 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 411/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, 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: 4.513e-04, size: 448, ETA: 1:02:46
2025-09-01 15:53:14.010 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:53:20.181 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:53:21.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:53:22.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6068
2025-09-01 15:53:22.193 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5148
2025-09-01 15:53:22.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3882
2025-09-01 15:53:22.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5033
2025-09-01 15:53:22.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:53:22.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:53:22.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-09-01 15:53:22.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 15:53:22.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-09-01 15:53:22.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-09-01 15:53:22.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:53:22.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:53:22.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:53:22.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:53:22.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:53:22.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:53:22.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:53:22.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:53:22.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:53:23.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:53:24.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:53:25.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:53:26.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:53:27.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:53:28.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:53:28.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:53:29.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:53:30.908 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:53:30.909 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:53:30.909 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:53:30.909 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:53:30.918 | 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

2025-09-01 15:53:30.919 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:53:31.009 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:53:31.099 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch412
2025-09-01 15:53:33.914 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 412/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.139s, data_time: 0.003s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.7, lr: 4.503e-04, size: 480, ETA: 1:02:41
2025-09-01 15:53:36.904 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 412/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 4.496e-04, size: 416, ETA: 1:02:38
2025-09-01 15:53:39.919 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 412/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 4.489e-04, size: 416, ETA: 1:02:35
2025-09-01 15:53:42.945 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 412/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 4.483e-04, size: 288, ETA: 1:02:32
2025-09-01 15:53:45.980 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 412/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.5, lr: 4.476e-04, size: 416, ETA: 1:02:28
2025-09-01 15:53:49.001 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 412/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 4.469e-04, size: 512, ETA: 1:02:25
2025-09-01 15:53:50.363 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:53:56.695 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:53:57.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:53:58.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6136
2025-09-01 15:53:58.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5243
2025-09-01 15:53:58.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3776
2025-09-01 15:53:58.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5051
2025-09-01 15:53:58.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:53:58.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:53:58.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.614
2025-09-01 15:53:58.296 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-09-01 15:53:58.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-09-01 15:53:58.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-09-01 15:53:58.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:53:58.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:53:58.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:53:58.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:53:58.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:53:58.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:53:58.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:53:58.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:53:58.298 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:53:59.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:53:59.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:54:00.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:54:01.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:54:02.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:54:02.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:54:03.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:54:04.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:54:05.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:54:05.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:54:05.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:54:05.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:54:05.044 | 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: 7.08 ms

2025-09-01 15:54:05.044 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:54:05.134 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:54:05.224 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch413
2025-09-01 15:54:08.163 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 413/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.7, iou_loss: 1.8, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.5, lr: 4.459e-04, size: 512, ETA: 1:02:21
2025-09-01 15:54:11.155 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 413/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 9.2, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 4.0, cls_loss: 0.8, lr: 4.453e-04, size: 352, ETA: 1:02:18
2025-09-01 15:54:14.191 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 413/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 4.446e-04, size: 448, ETA: 1:02:14
2025-09-01 15:54:17.155 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 413/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 4.439e-04, size: 352, ETA: 1:02:11
2025-09-01 15:54:20.214 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 413/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 4.432e-04, size: 480, ETA: 1:02:08
2025-09-01 15:54:23.273 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 413/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.9, l1_loss: 0.8, conf_loss: 3.4, cls_loss: 0.7, lr: 4.426e-04, size: 288, ETA: 1:02:05
2025-09-01 15:54:24.655 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:54:30.774 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:54:31.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:54:32.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6015
2025-09-01 15:54:32.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5168
2025-09-01 15:54:32.345 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3820
2025-09-01 15:54:32.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5001
2025-09-01 15:54:32.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:54:32.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:54:32.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.602
2025-09-01 15:54:32.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 15:54:32.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-09-01 15:54:32.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-09-01 15:54:32.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:54:32.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:54:32.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:54:32.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:54:32.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:54:32.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:54:32.349 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:54:32.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:54:32.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:54:33.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:54:33.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:54:34.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:54:35.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:54:35.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:54:36.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:54:37.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:54:37.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:54:38.525 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:54:38.525 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:54:38.525 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:54:38.525 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:54:38.533 | 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

2025-09-01 15:54:38.534 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:54:38.623 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:54:38.704 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch414
2025-09-01 15:54:41.551 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 414/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.6, lr: 4.416e-04, size: 416, ETA: 1:02:00
2025-09-01 15:54:44.596 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 414/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 1.9, cls_loss: 0.9, lr: 4.409e-04, size: 320, ETA: 1:01:57
2025-09-01 15:54:47.598 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 414/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 4.402e-04, size: 384, ETA: 1:01:54
2025-09-01 15:54:50.547 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 414/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 2.2, cls_loss: 0.6, lr: 4.396e-04, size: 544, ETA: 1:01:51
2025-09-01 15:54:53.526 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 414/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.7, lr: 4.389e-04, size: 384, ETA: 1:01:48
2025-09-01 15:54:56.523 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 414/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 4.382e-04, size: 448, ETA: 1:01:45
2025-09-01 15:54:57.951 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:55:04.329 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:55:05.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:55:05.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5990
2025-09-01 15:55:05.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5173
2025-09-01 15:55:05.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3958
2025-09-01 15:55:05.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5040
2025-09-01 15:55:05.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:55:05.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:55:05.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-09-01 15:55:05.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 15:55:05.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-09-01 15:55:05.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-09-01 15:55:05.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:55:05.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:55:05.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:55:05.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:55:05.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:55:05.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:55:05.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:55:05.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:55:05.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:55:06.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:55:07.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:55:07.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:55:08.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:55:09.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:55:09.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:55:10.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:55:11.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:55:11.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:55:11.999 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:55:11.999 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:55:11.999 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:55:12.006 | 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: 7.09 ms

2025-09-01 15:55:12.007 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:55:12.087 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:55:12.170 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch415
2025-09-01 15:55:14.962 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 415/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.138s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 4.372e-04, size: 256, ETA: 1:01:40
2025-09-01 15:55:18.051 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 415/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, 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: 4.366e-04, size: 448, ETA: 1:01:37
2025-09-01 15:55:21.044 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 415/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 1.4, conf_loss: 1.4, cls_loss: 0.6, lr: 4.359e-04, size: 544, ETA: 1:01:34
2025-09-01 15:55:24.061 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 415/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 1.5, conf_loss: 2.2, cls_loss: 0.7, lr: 4.352e-04, size: 576, ETA: 1:01:31
2025-09-01 15:55:27.128 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 415/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, 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: 4.346e-04, size: 512, ETA: 1:01:28
2025-09-01 15:55:30.111 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 415/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 4.339e-04, size: 416, ETA: 1:01:24
2025-09-01 15:55:31.436 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:55:37.668 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:55:38.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:55:39.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5932
2025-09-01 15:55:39.491 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5261
2025-09-01 15:55:39.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3948
2025-09-01 15:55:39.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5047
2025-09-01 15:55:39.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:55:39.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:55:39.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-09-01 15:55:39.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-09-01 15:55:39.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-09-01 15:55:39.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-09-01 15:55:39.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:55:39.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:55:39.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:55:39.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:55:39.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:55:39.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:55:39.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:55:39.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:55:39.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:55:40.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:55:41.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:55:42.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:55:43.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:55:43.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:55:44.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:55:45.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:55:46.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:55:47.364 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:55:47.365 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:55:47.365 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:55:47.365 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:55:47.372 | 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: 7.16 ms

2025-09-01 15:55:47.373 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:55:47.467 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:55:47.549 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch416
2025-09-01 15:55:50.587 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 416/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 1.3, cls_loss: 0.6, lr: 4.329e-04, size: 544, ETA: 1:01:20
2025-09-01 15:55:53.681 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 416/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.5, lr: 4.323e-04, size: 544, ETA: 1:01:17
2025-09-01 15:55:56.613 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 416/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 4.316e-04, size: 320, ETA: 1:01:14
2025-09-01 15:55:59.705 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 416/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.1, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.6, cls_loss: 0.7, lr: 4.309e-04, size: 576, ETA: 1:01:10
2025-09-01 15:56:02.777 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 416/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, 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: 4.303e-04, size: 512, ETA: 1:01:07
2025-09-01 15:56:05.812 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 416/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, 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: 4.296e-04, size: 544, ETA: 1:01:04
2025-09-01 15:56:07.199 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:56:13.397 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:56:14.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:56:14.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5957
2025-09-01 15:56:14.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5306
2025-09-01 15:56:14.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3780
2025-09-01 15:56:14.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5014
2025-09-01 15:56:14.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:56:14.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:56:14.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.596
2025-09-01 15:56:14.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-09-01 15:56:14.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-09-01 15:56:14.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-09-01 15:56:14.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:56:14.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:56:14.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:56:14.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:56:14.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:56:14.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:56:14.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:56:14.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:56:14.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:56:15.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:56:16.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:56:16.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:56:17.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:56:17.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:56:18.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:56:19.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:56:19.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:56:20.232 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:56:20.232 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:56:20.232 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:56:20.232 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:56:20.239 | 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

2025-09-01 15:56:20.240 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:56:20.325 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:56:20.409 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch417
2025-09-01 15:56:23.513 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 417/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.005s, total_loss: 4.3, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.6, lr: 4.286e-04, size: 288, ETA: 1:01:00
2025-09-01 15:56:26.467 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 417/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.8, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.7, lr: 4.280e-04, size: 288, ETA: 1:00:57
2025-09-01 15:56:29.462 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 417/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.8, lr: 4.273e-04, size: 448, ETA: 1:00:53
2025-09-01 15:56:32.546 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 417/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.9, cls_loss: 0.8, lr: 4.266e-04, size: 544, ETA: 1:00:50
2025-09-01 15:56:35.597 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 417/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 1.0, cls_loss: 1.0, lr: 4.260e-04, size: 256, ETA: 1:00:47
2025-09-01 15:56:38.650 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 417/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.9, lr: 4.253e-04, size: 320, ETA: 1:00:44
2025-09-01 15:56:40.023 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:56:46.196 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:56:47.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:56:47.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5964
2025-09-01 15:56:47.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5092
2025-09-01 15:56:47.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3923
2025-09-01 15:56:47.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4993
2025-09-01 15:56:47.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:56:47.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:56:47.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.596
2025-09-01 15:56:47.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-09-01 15:56:47.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-09-01 15:56:47.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.499
2025-09-01 15:56:47.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:56:47.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:56:47.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:56:47.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:56:47.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:56:47.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:56:47.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:56:47.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:56:47.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:56:48.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:56:49.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:56:50.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:56:51.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:56:51.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:56:52.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:56:53.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:56:54.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:56:55.042 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:56:55.042 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:56:55.042 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:56:55.042 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:56:55.049 | 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: 7.08 ms

2025-09-01 15:56:55.051 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:56:55.180 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:56:55.284 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch418
2025-09-01 15:56:58.259 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 418/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.8, cls_loss: 1.1, lr: 4.243e-04, size: 288, ETA: 1:00:40
2025-09-01 15:57:01.210 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 418/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 4.237e-04, size: 480, ETA: 1:00:36
2025-09-01 15:57:04.350 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 418/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 0.9, cls_loss: 0.6, lr: 4.230e-04, size: 320, ETA: 1:00:33
2025-09-01 15:57:07.396 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 418/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 4.223e-04, size: 384, ETA: 1:00:30
2025-09-01 15:57:10.440 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 418/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.9, cls_loss: 0.5, lr: 4.217e-04, size: 384, ETA: 1:00:27
2025-09-01 15:57:13.400 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 418/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 2.8, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 4.210e-04, size: 416, ETA: 1:00:24
2025-09-01 15:57:14.742 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:57:21.164 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:57:21.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:57:22.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6114
2025-09-01 15:57:22.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5278
2025-09-01 15:57:22.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4002
2025-09-01 15:57:22.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5131
2025-09-01 15:57:22.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:57:22.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:57:22.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.611
2025-09-01 15:57:22.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-09-01 15:57:22.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-09-01 15:57:22.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-09-01 15:57:22.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:57:22.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:57:22.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:57:22.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:57:22.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:57:22.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:57:22.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:57:22.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:57:22.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:57:23.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:57:24.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:57:24.694 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:57:25.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:57:26.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:57:26.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:57:27.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:57:28.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:57:28.790 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:57:28.790 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:57:28.790 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:57:28.790 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:57:28.798 | 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: 7.17 ms

2025-09-01 15:57:28.798 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:57:28.883 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:57:28.967 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch419
2025-09-01 15:57:31.939 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 419/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.005s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 4.201e-04, size: 416, ETA: 1:00:19
2025-09-01 15:57:34.922 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 419/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.9, lr: 4.194e-04, size: 448, ETA: 1:00:16
2025-09-01 15:57:37.904 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 419/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 4.187e-04, size: 448, ETA: 1:00:13
2025-09-01 15:57:40.921 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 419/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 3.1, iou_loss: 1.3, l1_loss: 0.4, conf_loss: 0.9, cls_loss: 0.4, lr: 4.181e-04, size: 288, ETA: 1:00:10
2025-09-01 15:57:43.971 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 419/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.8, lr: 4.174e-04, size: 320, ETA: 1:00:07
2025-09-01 15:57:46.975 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 419/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 4.168e-04, size: 544, ETA: 1:00:04
2025-09-01 15:57:48.360 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:57:54.566 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:57:55.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:57:55.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6036
2025-09-01 15:57:55.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5205
2025-09-01 15:57:55.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3696
2025-09-01 15:57:55.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4979
2025-09-01 15:57:55.637 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:57:55.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:57:55.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-09-01 15:57:55.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 15:57:55.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-09-01 15:57:55.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.498
2025-09-01 15:57:55.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:57:55.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:57:55.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:57:55.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:57:55.638 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:57:55.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:57:55.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:57:55.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:57:55.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:57:56.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:57:56.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:57:57.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:57:57.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:57:58.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:57:58.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:57:59.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:57:59.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:58:00.092 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:58:00.092 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:58:00.092 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:58:00.092 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:58:00.099 | 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

2025-09-01 15:58:00.101 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:58:00.188 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:58:00.308 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch420
2025-09-01 15:58:03.265 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 420/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, 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: 4.158e-04, size: 352, ETA: 0:59:59
2025-09-01 15:58:06.299 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 420/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 4.151e-04, size: 480, ETA: 0:59:56
2025-09-01 15:58:09.433 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 420/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 4.145e-04, size: 288, ETA: 0:59:53
2025-09-01 15:58:12.458 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 420/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 3.0, cls_loss: 0.6, lr: 4.138e-04, size: 352, ETA: 0:59:50
2025-09-01 15:58:15.577 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 420/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 4.132e-04, size: 448, ETA: 0:59:47
2025-09-01 15:58:18.564 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 420/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.5, lr: 4.125e-04, size: 544, ETA: 0:59:43
2025-09-01 15:58:19.962 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:58:26.146 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:58:26.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:58:27.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6087
2025-09-01 15:58:27.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5172
2025-09-01 15:58:27.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3858
2025-09-01 15:58:27.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5039
2025-09-01 15:58:27.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:58:27.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:58:27.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-09-01 15:58:27.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 15:58:27.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.386
2025-09-01 15:58:27.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-09-01 15:58:27.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:58:27.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:58:27.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:58:27.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:58:27.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:58:27.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:58:27.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:58:27.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:58:27.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:58:28.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:58:28.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:58:29.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:58:30.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:58:30.705 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:58:31.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:58:31.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:58:32.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:58:33.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:58:33.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 15:58:33.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 15:58:33.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:58:33.202 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.33 ms, Average NMS time: 0.90 ms, Average inference time: 7.23 ms

2025-09-01 15:58:33.203 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:58:33.290 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:58:33.373 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch421
2025-09-01 15:58:36.294 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 421/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 4.116e-04, size: 288, ETA: 0:59:39
2025-09-01 15:58:39.284 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 421/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.7, lr: 4.109e-04, size: 320, ETA: 0:59:36
2025-09-01 15:58:42.351 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 421/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.5, lr: 4.102e-04, size: 320, ETA: 0:59:33
2025-09-01 15:58:45.327 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 421/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 4.096e-04, size: 416, ETA: 0:59:30
2025-09-01 15:58:48.351 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 421/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, 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: 4.089e-04, size: 512, ETA: 0:59:26
2025-09-01 15:58:51.356 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 421/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 4.083e-04, size: 352, ETA: 0:59:23
2025-09-01 15:58:52.674 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:58:58.723 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:58:59.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:58:59.636 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5909
2025-09-01 15:58:59.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4942
2025-09-01 15:58:59.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3527
2025-09-01 15:58:59.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4793
2025-09-01 15:58:59.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:58:59.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:58:59.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.591
2025-09-01 15:58:59.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.494
2025-09-01 15:58:59.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.353
2025-09-01 15:58:59.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.479
2025-09-01 15:58:59.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:58:59.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:58:59.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:58:59.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:58:59.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:58:59.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:58:59.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:58:59.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:58:59.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:59:00.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:59:00.843 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:59:01.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:59:01.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:59:02.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:59:02.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:59:03.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:59:03.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:59:04.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:59:04.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 15:59:04.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 15:59:04.015 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:59:04.022 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.25 ms, Average NMS time: 0.87 ms, Average inference time: 7.12 ms

2025-09-01 15:59:04.023 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:59:04.105 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:59:04.187 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch422
2025-09-01 15:59:07.099 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 422/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 4.073e-04, size: 480, ETA: 0:59:19
2025-09-01 15:59:10.116 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 422/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.9, iou_loss: 1.9, l1_loss: 1.4, conf_loss: 3.0, cls_loss: 0.5, lr: 4.067e-04, size: 416, ETA: 0:59:16
2025-09-01 15:59:13.244 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 422/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, 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: 4.060e-04, size: 256, ETA: 0:59:12
2025-09-01 15:59:16.127 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 422/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 4.054e-04, size: 288, ETA: 0:59:09
2025-09-01 15:59:19.203 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 422/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 4.047e-04, size: 544, ETA: 0:59:06
2025-09-01 15:59:22.152 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 422/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.7, lr: 4.041e-04, size: 320, ETA: 0:59:03
2025-09-01 15:59:23.475 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:59:29.567 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 15:59:30.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 15:59:31.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6138
2025-09-01 15:59:31.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5203
2025-09-01 15:59:31.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4039
2025-09-01 15:59:31.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5127
2025-09-01 15:59:31.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 15:59:31.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 15:59:31.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.614
2025-09-01 15:59:31.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-09-01 15:59:31.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.404
2025-09-01 15:59:31.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-09-01 15:59:31.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 15:59:31.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 15:59:31.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 15:59:31.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 15:59:31.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 15:59:31.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 15:59:31.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 15:59:31.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 15:59:31.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 15:59:31.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 15:59:32.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 15:59:33.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 15:59:34.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 15:59:34.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 15:59:35.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 15:59:36.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 15:59:37.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 15:59:37.993 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 15:59:37.993 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.28
2025-09-01 15:59:37.993 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 15:59:37.993 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 15:59:38.000 | 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

2025-09-01 15:59:38.001 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:59:38.094 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 15:59:38.176 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch423
2025-09-01 15:59:41.096 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 423/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, 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: 4.031e-04, size: 352, ETA: 0:58:58
2025-09-01 15:59:44.006 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 423/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, 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: 4.025e-04, size: 512, ETA: 0:58:55
2025-09-01 15:59:47.058 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 423/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 10.0, iou_loss: 3.2, l1_loss: 1.0, conf_loss: 5.0, cls_loss: 0.8, lr: 4.018e-04, size: 416, ETA: 0:58:52
2025-09-01 15:59:50.202 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 423/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 4.012e-04, size: 256, ETA: 0:58:49
2025-09-01 15:59:53.273 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 423/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.2, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 3.5, cls_loss: 0.8, lr: 4.005e-04, size: 384, ETA: 0:58:46
2025-09-01 15:59:56.291 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 423/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, 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: 3.999e-04, size: 576, ETA: 0:58:43
2025-09-01 15:59:57.690 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:00:03.961 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:00:04.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:00:05.352 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5915
2025-09-01 16:00:05.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4889
2025-09-01 16:00:05.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3891
2025-09-01 16:00:05.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4898
2025-09-01 16:00:05.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:00:05.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:00:05.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.591
2025-09-01 16:00:05.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-09-01 16:00:05.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-09-01 16:00:05.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-09-01 16:00:05.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:00:05.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:00:05.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:00:05.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:00:05.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:00:05.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:00:05.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:00:05.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:00:05.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:00:06.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:00:07.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:00:07.709 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:00:08.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:00:09.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:00:09.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:00:10.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:00:11.239 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:00:11.927 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:00:11.928 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 16:00:11.928 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 16:00:11.928 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:00:11.935 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.89 ms, Average inference time: 7.11 ms

2025-09-01 16:00:11.937 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:00:12.015 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:00:12.100 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch424
2025-09-01 16:00:14.990 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 424/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, data_time: 0.003s, total_loss: 9.4, iou_loss: 3.0, l1_loss: 1.8, conf_loss: 3.8, cls_loss: 0.8, lr: 3.989e-04, size: 416, ETA: 0:58:38
2025-09-01 16:00:18.149 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 424/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.6, lr: 3.983e-04, size: 576, ETA: 0:58:35
2025-09-01 16:00:21.245 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 424/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 1.0, lr: 3.976e-04, size: 288, ETA: 0:58:32
2025-09-01 16:00:24.221 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 424/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 3.970e-04, size: 352, ETA: 0:58:29
2025-09-01 16:00:27.444 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 424/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 3.963e-04, size: 448, ETA: 0:58:26
2025-09-01 16:00:30.395 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 424/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 3.957e-04, size: 288, ETA: 0:58:23
2025-09-01 16:00:31.763 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:00:37.953 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:00:38.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:00:39.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5949
2025-09-01 16:00:39.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5122
2025-09-01 16:00:39.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3938
2025-09-01 16:00:39.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5003
2025-09-01 16:00:39.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:00:39.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:00:39.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.595
2025-09-01 16:00:39.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-09-01 16:00:39.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-09-01 16:00:39.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-09-01 16:00:39.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:00:39.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:00:39.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:00:39.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:00:39.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:00:39.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:00:39.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:00:39.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:00:39.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:00:39.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:00:40.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:00:41.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:00:41.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:00:42.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:00:42.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:00:43.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:00:43.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:00:44.488 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:00:44.488 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:00:44.488 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:00:44.488 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:00:44.496 | 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

2025-09-01 16:00:44.496 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:00:44.633 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:00:44.708 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch425
2025-09-01 16:00:47.751 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 425/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.6, lr: 3.948e-04, size: 352, ETA: 0:58:18
2025-09-01 16:00:50.794 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 425/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 12.8, iou_loss: 3.7, l1_loss: 1.3, conf_loss: 6.8, cls_loss: 0.9, lr: 3.941e-04, size: 416, ETA: 0:58:15
2025-09-01 16:00:53.904 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 425/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.1, cls_loss: 0.6, lr: 3.935e-04, size: 576, ETA: 0:58:12
2025-09-01 16:00:57.032 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 425/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 3.928e-04, size: 448, ETA: 0:58:09
2025-09-01 16:01:00.098 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 425/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 3.922e-04, size: 416, ETA: 0:58:06
2025-09-01 16:01:03.166 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 425/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, 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: 3.915e-04, size: 448, ETA: 0:58:03
2025-09-01 16:01:04.596 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:01:10.830 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:01:11.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:01:12.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6020
2025-09-01 16:01:12.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5165
2025-09-01 16:01:12.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3924
2025-09-01 16:01:12.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5037
2025-09-01 16:01:12.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:01:12.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:01:12.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.602
2025-09-01 16:01:12.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 16:01:12.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-09-01 16:01:12.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-09-01 16:01:12.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:01:12.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:01:12.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:01:12.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:01:12.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:01:12.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:01:12.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:01:12.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:01:12.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:01:13.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:01:13.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:01:14.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:01:15.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:01:16.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:01:16.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:01:17.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:01:18.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:01:19.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:01:19.174 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:01:19.174 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:01:19.174 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:01:19.181 | 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

2025-09-01 16:01:19.186 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:01:19.275 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:01:19.354 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch426
2025-09-01 16:01:22.213 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 426/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 3.906e-04, size: 544, ETA: 0:57:58
2025-09-01 16:01:25.468 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 426/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.161s, 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: 3.900e-04, size: 256, ETA: 0:57:55
2025-09-01 16:01:28.496 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 426/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 1.3, conf_loss: 1.5, cls_loss: 0.6, lr: 3.893e-04, size: 544, ETA: 0:57:52
2025-09-01 16:01:31.521 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 426/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 0.8, cls_loss: 0.6, lr: 3.887e-04, size: 480, ETA: 0:57:49
2025-09-01 16:01:34.530 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 426/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.8, lr: 3.880e-04, size: 288, ETA: 0:57:46
2025-09-01 16:01:37.562 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 426/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, 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: 3.874e-04, size: 448, ETA: 0:57:43
2025-09-01 16:01:39.007 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:01:45.152 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:01:45.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:01:46.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6072
2025-09-01 16:01:46.624 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5312
2025-09-01 16:01:46.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4205
2025-09-01 16:01:46.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5196
2025-09-01 16:01:46.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:01:46.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:01:46.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-09-01 16:01:46.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-09-01 16:01:46.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.421
2025-09-01 16:01:46.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.520
2025-09-01 16:01:46.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:01:46.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:01:46.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:01:46.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:01:46.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:01:46.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:01:46.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:01:46.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:01:46.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:01:47.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:01:48.064 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:01:48.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:01:49.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:01:50.109 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:01:50.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:01:51.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:01:52.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:01:52.856 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:01:52.857 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:01:52.857 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 16:01:52.857 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:01:52.864 | 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

2025-09-01 16:01:52.865 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:01:52.952 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:01:53.033 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch427
2025-09-01 16:01:55.888 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 427/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 10.2, iou_loss: 3.1, l1_loss: 1.3, conf_loss: 5.2, cls_loss: 0.7, lr: 3.865e-04, size: 416, ETA: 0:57:38
2025-09-01 16:01:59.042 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 427/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 3.858e-04, size: 576, ETA: 0:57:35
2025-09-01 16:02:02.088 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 427/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 3.852e-04, size: 480, ETA: 0:57:32
2025-09-01 16:02:05.077 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 427/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, 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: 3.845e-04, size: 512, ETA: 0:57:29
2025-09-01 16:02:08.148 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 427/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, 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: 3.839e-04, size: 576, ETA: 0:57:26
2025-09-01 16:02:11.255 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 427/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 3.833e-04, size: 288, ETA: 0:57:23
2025-09-01 16:02:12.687 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:02:18.770 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:02:19.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:02:20.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5934
2025-09-01 16:02:20.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5023
2025-09-01 16:02:20.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3856
2025-09-01 16:02:20.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4938
2025-09-01 16:02:20.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:02:20.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:02:20.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-09-01 16:02:20.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.502
2025-09-01 16:02:20.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.386
2025-09-01 16:02:20.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-09-01 16:02:20.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:02:20.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:02:20.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:02:20.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:02:20.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:02:20.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:02:20.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:02:20.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:02:20.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:02:21.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:02:22.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:02:23.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:02:23.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:02:24.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:02:25.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:02:26.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:02:27.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:02:27.988 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:02:27.989 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 16:02:27.989 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 16:02:27.989 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:02:27.996 | 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: 7.08 ms

2025-09-01 16:02:27.997 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:02:28.084 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:02:28.166 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch428
2025-09-01 16:02:31.104 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 428/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.1, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.6, lr: 3.823e-04, size: 288, ETA: 0:57:18
2025-09-01 16:02:34.140 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 428/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.7, lr: 3.817e-04, size: 384, ETA: 0:57:15
2025-09-01 16:02:37.160 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 428/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.6, lr: 3.810e-04, size: 480, ETA: 0:57:12
2025-09-01 16:02:40.234 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 428/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 3.804e-04, size: 512, ETA: 0:57:09
2025-09-01 16:02:43.154 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 428/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 3.798e-04, size: 384, ETA: 0:57:05
2025-09-01 16:02:46.168 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 428/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 3.791e-04, size: 416, ETA: 0:57:02
2025-09-01 16:02:47.527 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:02:53.931 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:02:54.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:02:55.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6110
2025-09-01 16:02:55.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5164
2025-09-01 16:02:55.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4053
2025-09-01 16:02:55.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5109
2025-09-01 16:02:55.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:02:55.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:02:55.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.611
2025-09-01 16:02:55.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-09-01 16:02:55.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-09-01 16:02:55.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-09-01 16:02:55.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:02:55.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:02:55.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:02:55.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:02:55.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:02:55.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:02:55.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:02:55.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:02:55.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:02:56.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:02:56.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:02:57.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:02:58.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:02:58.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:02:59.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:03:00.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:03:00.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:03:01.567 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:03:01.567 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:03:01.567 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:03:01.568 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:03:01.575 | 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

2025-09-01 16:03:01.576 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:03:01.658 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:03:01.741 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch429
2025-09-01 16:03:04.732 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 429/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, 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: 3.782e-04, size: 576, ETA: 0:56:58
2025-09-01 16:03:07.852 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 429/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.6, lr: 3.776e-04, size: 480, ETA: 0:56:55
2025-09-01 16:03:10.856 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 429/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 3.769e-04, size: 512, ETA: 0:56:52
2025-09-01 16:03:13.963 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 429/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 3.763e-04, size: 384, ETA: 0:56:48
2025-09-01 16:03:16.891 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 429/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.5, lr: 3.757e-04, size: 448, ETA: 0:56:45
2025-09-01 16:03:19.959 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 429/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, 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: 3.750e-04, size: 416, ETA: 0:56:42
2025-09-01 16:03:21.352 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:03:27.505 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:03:28.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:03:29.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6042
2025-09-01 16:03:29.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5300
2025-09-01 16:03:29.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4023
2025-09-01 16:03:29.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5121
2025-09-01 16:03:29.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:03:29.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:03:29.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-09-01 16:03:29.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-09-01 16:03:29.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-09-01 16:03:29.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.512
2025-09-01 16:03:29.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:03:29.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:03:29.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:03:29.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:03:29.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:03:29.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:03:29.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:03:29.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:03:29.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:03:30.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:03:31.285 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:03:32.206 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:03:33.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:03:34.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:03:34.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:03:35.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:03:36.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:03:37.574 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:03:37.575 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:03:37.575 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:03:37.575 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:03:37.582 | 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: 7.15 ms

2025-09-01 16:03:37.583 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:03:37.682 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:03:37.763 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch430
2025-09-01 16:03:40.645 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 430/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 3.741e-04, size: 512, ETA: 0:56:38
2025-09-01 16:03:43.766 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 430/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 3.735e-04, size: 544, ETA: 0:56:35
2025-09-01 16:03:46.827 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 430/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 1.3, lr: 3.729e-04, size: 512, ETA: 0:56:31
2025-09-01 16:03:50.003 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 430/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 0.8, lr: 3.722e-04, size: 416, ETA: 0:56:28
2025-09-01 16:03:53.015 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 430/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 3.716e-04, size: 448, ETA: 0:56:25
2025-09-01 16:03:56.160 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 430/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, 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: 3.710e-04, size: 512, ETA: 0:56:22
2025-09-01 16:03:57.588 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:04:03.818 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:04:04.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:04:05.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6039
2025-09-01 16:04:05.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5216
2025-09-01 16:04:05.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3917
2025-09-01 16:04:05.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5058
2025-09-01 16:04:05.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:04:05.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:04:05.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-09-01 16:04:05.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-09-01 16:04:05.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-09-01 16:04:05.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.506
2025-09-01 16:04:05.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:04:05.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:04:05.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:04:05.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:04:05.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:04:05.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:04:05.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:04:05.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:04:05.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:04:05.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:04:06.378 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:04:06.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:04:07.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:04:08.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:04:08.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:04:09.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:04:10.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:04:10.707 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:04:10.707 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:04:10.707 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:04:10.707 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:04:10.714 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.87 ms, Average inference time: 7.00 ms

2025-09-01 16:04:10.715 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:04:10.800 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:04:10.884 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch431
2025-09-01 16:04:13.691 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 431/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.5, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 3.700e-04, size: 480, ETA: 0:56:18
2025-09-01 16:04:16.793 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 431/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 3.3, iou_loss: 1.2, l1_loss: 0.4, conf_loss: 1.3, cls_loss: 0.5, lr: 3.694e-04, size: 544, ETA: 0:56:15
2025-09-01 16:04:19.932 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 431/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 3.688e-04, size: 416, ETA: 0:56:11
2025-09-01 16:04:22.877 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 431/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.6, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.2, cls_loss: 0.8, lr: 3.682e-04, size: 416, ETA: 0:56:08
2025-09-01 16:04:25.845 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 431/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 2.2, cls_loss: 0.6, lr: 3.675e-04, size: 320, ETA: 0:56:05
2025-09-01 16:04:28.863 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 431/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.0, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.8, lr: 3.669e-04, size: 352, ETA: 0:56:02
2025-09-01 16:04:30.188 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:04:36.632 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:04:37.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:04:38.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6035
2025-09-01 16:04:38.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5240
2025-09-01 16:04:38.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3811
2025-09-01 16:04:38.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5029
2025-09-01 16:04:38.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:04:38.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:04:38.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-09-01 16:04:38.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-09-01 16:04:38.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-09-01 16:04:38.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-09-01 16:04:38.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:04:38.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:04:38.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:04:38.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:04:38.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:04:38.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:04:38.279 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:04:38.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:04:38.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:04:39.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:04:39.836 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:04:40.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:04:41.346 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:04:42.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:04:42.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:04:43.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:04:44.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:04:45.095 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:04:45.095 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 16:04:45.095 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:04:45.096 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:04:45.109 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.91 ms, Average inference time: 7.22 ms

2025-09-01 16:04:45.110 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:04:45.230 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:04:45.337 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch432
2025-09-01 16:04:48.281 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 432/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, 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: 3.660e-04, size: 480, ETA: 0:55:57
2025-09-01 16:04:51.350 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 432/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 3.654e-04, size: 480, ETA: 0:55:54
2025-09-01 16:04:54.403 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 432/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.5, lr: 3.647e-04, size: 384, ETA: 0:55:51
2025-09-01 16:04:57.574 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 432/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.7, lr: 3.641e-04, size: 320, ETA: 0:55:48
2025-09-01 16:05:00.602 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 432/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.9, lr: 3.635e-04, size: 544, ETA: 0:55:45
2025-09-01 16:05:03.615 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 432/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.6, lr: 3.629e-04, size: 288, ETA: 0:55:42
2025-09-01 16:05:04.946 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:05:11.485 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:05:12.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:05:13.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6041
2025-09-01 16:05:13.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5306
2025-09-01 16:05:13.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3987
2025-09-01 16:05:13.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5111
2025-09-01 16:05:13.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:05:13.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:05:13.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-09-01 16:05:13.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-09-01 16:05:13.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-09-01 16:05:13.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-09-01 16:05:13.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:05:13.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:05:13.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:05:13.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:05:13.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:05:13.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:05:13.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:05:13.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:05:13.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:05:14.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:05:14.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:05:15.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:05:16.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:05:17.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:05:17.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:05:18.654 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:05:19.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:05:20.149 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:05:20.149 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:05:20.150 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:05:20.150 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:05:20.157 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.19 ms, Average NMS time: 0.86 ms, Average inference time: 7.05 ms

2025-09-01 16:05:20.158 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:05:20.309 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:05:20.383 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch433
2025-09-01 16:05:23.384 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 433/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 1.1, lr: 3.620e-04, size: 320, ETA: 0:55:37
2025-09-01 16:05:26.559 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 433/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.5, lr: 3.613e-04, size: 448, ETA: 0:55:34
2025-09-01 16:05:29.639 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 433/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 3.607e-04, size: 448, ETA: 0:55:31
2025-09-01 16:05:32.637 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 433/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 1.3, cls_loss: 0.5, lr: 3.601e-04, size: 384, ETA: 0:55:28
2025-09-01 16:05:35.566 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 433/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 1.0, lr: 3.595e-04, size: 416, ETA: 0:55:25
2025-09-01 16:05:38.467 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 433/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.5, lr: 3.588e-04, size: 544, ETA: 0:55:22
2025-09-01 16:05:39.870 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:05:45.977 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:05:46.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:05:46.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5854
2025-09-01 16:05:46.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4662
2025-09-01 16:05:46.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3297
2025-09-01 16:05:46.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4604
2025-09-01 16:05:46.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:05:46.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:05:46.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.585
2025-09-01 16:05:46.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.466
2025-09-01 16:05:46.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.330
2025-09-01 16:05:46.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.460
2025-09-01 16:05:46.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:05:46.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:05:46.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:05:46.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:05:46.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:05:46.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:05:46.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:05:46.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:05:46.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:05:47.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:05:47.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:05:47.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:05:48.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:05:48.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:05:48.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:05:49.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:05:49.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:05:49.872 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:05:49.872 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 16:05:49.872 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 16:05:49.872 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:05:49.879 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.37 ms, Average NMS time: 0.80 ms, Average inference time: 7.17 ms

2025-09-01 16:05:49.879 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:05:49.975 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:05:50.052 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch434
2025-09-01 16:05:53.039 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 434/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.5, lr: 3.579e-04, size: 544, ETA: 0:55:17
2025-09-01 16:05:56.070 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 434/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.9, lr: 3.573e-04, size: 288, ETA: 0:55:14
2025-09-01 16:05:59.222 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 434/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 3.1, iou_loss: 0.9, l1_loss: 0.4, conf_loss: 1.4, cls_loss: 0.4, lr: 3.567e-04, size: 320, ETA: 0:55:11
2025-09-01 16:06:02.437 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 434/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.6, lr: 3.561e-04, size: 352, ETA: 0:55:08
2025-09-01 16:06:05.581 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 434/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, 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: 3.554e-04, size: 384, ETA: 0:55:05
2025-09-01 16:06:08.603 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 434/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.8, lr: 3.548e-04, size: 256, ETA: 0:55:02
2025-09-01 16:06:09.930 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:06:16.117 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:06:16.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:06:17.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5816
2025-09-01 16:06:17.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5025
2025-09-01 16:06:17.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3855
2025-09-01 16:06:17.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4899
2025-09-01 16:06:17.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:06:17.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:06:17.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.582
2025-09-01 16:06:17.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.503
2025-09-01 16:06:17.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.386
2025-09-01 16:06:17.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.490
2025-09-01 16:06:17.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:06:17.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:06:17.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:06:17.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:06:17.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:06:17.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:06:17.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:06:17.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:06:17.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:06:17.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:06:17.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:06:18.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:06:18.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:06:19.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:06:19.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:06:19.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:06:20.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:06:20.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:06:20.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:06:20.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 16:06:20.804 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:06:20.811 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.37 ms, Average NMS time: 0.86 ms, Average inference time: 7.23 ms

2025-09-01 16:06:20.812 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:06:20.901 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:06:20.982 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch435
2025-09-01 16:06:23.918 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 435/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.6, lr: 3.539e-04, size: 512, ETA: 0:54:57
2025-09-01 16:06:26.982 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 435/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 3.533e-04, size: 320, ETA: 0:54:54
2025-09-01 16:06:29.982 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 435/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.5, lr: 3.527e-04, size: 384, ETA: 0:54:51
2025-09-01 16:06:32.914 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 435/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.8, lr: 3.521e-04, size: 256, ETA: 0:54:48
2025-09-01 16:06:35.948 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 435/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, 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: 3.514e-04, size: 448, ETA: 0:54:45
2025-09-01 16:06:38.960 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 435/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.7, lr: 3.508e-04, size: 320, ETA: 0:54:42
2025-09-01 16:06:40.398 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:06:46.709 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:06:47.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:06:48.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6127
2025-09-01 16:06:48.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5001
2025-09-01 16:06:48.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3944
2025-09-01 16:06:48.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5024
2025-09-01 16:06:48.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:06:48.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:06:48.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.613
2025-09-01 16:06:48.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.500
2025-09-01 16:06:48.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-09-01 16:06:48.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 16:06:48.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:06:48.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:06:48.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:06:48.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:06:48.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:06:48.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:06:48.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:06:48.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:06:48.567 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:06:49.403 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:06:50.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:06:51.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:06:51.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:06:52.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:06:53.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:06:54.557 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:06:55.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:06:56.246 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:06:56.246 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:06:56.246 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:06:56.246 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:06:56.254 | 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: 7.15 ms

2025-09-01 16:06:56.260 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:06:56.350 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:06:56.431 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch436
2025-09-01 16:06:59.371 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 436/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 3.499e-04, size: 576, ETA: 0:54:37
2025-09-01 16:07:02.412 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 436/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 3.493e-04, size: 448, ETA: 0:54:34
2025-09-01 16:07:05.409 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 436/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.5, lr: 3.487e-04, size: 384, ETA: 0:54:31
2025-09-01 16:07:08.513 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 436/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 3.481e-04, size: 576, ETA: 0:54:28
2025-09-01 16:07:11.708 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 436/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 3.475e-04, size: 544, ETA: 0:54:25
2025-09-01 16:07:14.814 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 436/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 3.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 0.5, cls_loss: 0.6, lr: 3.469e-04, size: 288, ETA: 0:54:22
2025-09-01 16:07:16.105 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:07:22.320 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:07:22.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:07:23.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5732
2025-09-01 16:07:23.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4599
2025-09-01 16:07:23.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3859
2025-09-01 16:07:23.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4730
2025-09-01 16:07:23.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:07:23.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:07:23.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.573
2025-09-01 16:07:23.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.460
2025-09-01 16:07:23.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.386
2025-09-01 16:07:23.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.473
2025-09-01 16:07:23.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:07:23.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:07:23.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:07:23.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:07:23.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:07:23.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:07:23.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:07:23.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:07:23.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:07:23.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:07:23.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:07:24.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:07:24.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:07:25.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:07:25.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:07:25.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:07:26.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:07:26.470 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:07:26.471 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:07:26.471 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 16:07:26.471 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:07:26.477 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.86 ms, Average inference time: 7.08 ms

2025-09-01 16:07:26.478 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:07:26.613 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:07:26.686 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch437
2025-09-01 16:07:29.510 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 437/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.5, lr: 3.460e-04, size: 480, ETA: 0:54:17
2025-09-01 16:07:32.540 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 437/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, 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: 3.454e-04, size: 352, ETA: 0:54:14
2025-09-01 16:07:35.614 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 437/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.6, lr: 3.447e-04, size: 576, ETA: 0:54:11
2025-09-01 16:07:38.721 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 437/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 3.441e-04, size: 576, ETA: 0:54:08
2025-09-01 16:07:41.838 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 437/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, 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: 3.435e-04, size: 512, ETA: 0:54:05
2025-09-01 16:07:44.869 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 437/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.6, lr: 3.429e-04, size: 448, ETA: 0:54:01
2025-09-01 16:07:46.232 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:07:52.453 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:07:53.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:07:53.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5867
2025-09-01 16:07:54.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4918
2025-09-01 16:07:54.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3559
2025-09-01 16:07:54.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4781
2025-09-01 16:07:54.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:07:54.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:07:54.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.587
2025-09-01 16:07:54.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.492
2025-09-01 16:07:54.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-09-01 16:07:54.158 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.478
2025-09-01 16:07:54.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:07:54.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:07:54.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:07:54.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:07:54.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:07:54.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:07:54.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:07:54.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:07:54.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:07:54.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:07:55.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:07:56.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:07:57.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:07:57.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:07:58.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:07:59.493 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:08:00.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:08:01.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:08:01.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 16:08:01.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 16:08:01.036 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:08:01.043 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.13 ms, Average NMS time: 0.90 ms, Average inference time: 7.03 ms

2025-09-01 16:08:01.044 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:08:01.124 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:08:01.207 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch438
2025-09-01 16:08:04.145 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 438/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 8.4, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 3.3, cls_loss: 0.8, lr: 3.420e-04, size: 384, ETA: 0:53:57
2025-09-01 16:08:07.012 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 438/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.7, lr: 3.414e-04, size: 288, ETA: 0:53:54
2025-09-01 16:08:10.001 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 438/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, 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: 3.408e-04, size: 352, ETA: 0:53:51
2025-09-01 16:08:12.979 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 438/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.9, lr: 3.402e-04, size: 480, ETA: 0:53:47
2025-09-01 16:08:16.070 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 438/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 1.3, lr: 3.396e-04, size: 320, ETA: 0:53:44
2025-09-01 16:08:19.016 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 438/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.0, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.5, lr: 3.390e-04, size: 384, ETA: 0:53:41
2025-09-01 16:08:20.396 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:08:26.551 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:08:27.533 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:08:28.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6108
2025-09-01 16:08:28.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5283
2025-09-01 16:08:28.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3807
2025-09-01 16:08:28.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5066
2025-09-01 16:08:28.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:08:28.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:08:28.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.611
2025-09-01 16:08:28.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-09-01 16:08:28.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-09-01 16:08:28.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.507
2025-09-01 16:08:28.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:08:28.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:08:28.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:08:28.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:08:28.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:08:28.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:08:28.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:08:28.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:08:28.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:08:29.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:08:30.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:08:30.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:08:31.881 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:08:32.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:08:33.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:08:34.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:08:35.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:08:36.158 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:08:36.158 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:08:36.158 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:08:36.158 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:08:36.166 | 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

2025-09-01 16:08:36.171 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:08:36.303 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:08:36.378 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch439
2025-09-01 16:08:39.298 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 439/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.8, lr: 3.381e-04, size: 416, ETA: 0:53:37
2025-09-01 16:08:42.384 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 439/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.5, lr: 3.375e-04, size: 512, ETA: 0:53:34
2025-09-01 16:08:45.363 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 439/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.0, cls_loss: 0.6, lr: 3.369e-04, size: 320, ETA: 0:53:30
2025-09-01 16:08:48.448 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 439/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.6, lr: 3.363e-04, size: 576, ETA: 0:53:27
2025-09-01 16:08:51.686 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 439/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 3.356e-04, size: 256, ETA: 0:53:24
2025-09-01 16:08:54.712 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 439/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 3.350e-04, size: 416, ETA: 0:53:21
2025-09-01 16:08:56.061 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:09:02.268 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:09:02.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:09:03.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5737
2025-09-01 16:09:03.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5046
2025-09-01 16:09:03.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3825
2025-09-01 16:09:03.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4869
2025-09-01 16:09:03.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:09:03.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:09:03.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.574
2025-09-01 16:09:03.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-09-01 16:09:03.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-09-01 16:09:03.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-09-01 16:09:03.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:09:03.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:09:03.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:09:03.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:09:03.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:09:03.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:09:03.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:09:03.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:09:03.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:09:03.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:09:04.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:09:04.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:09:05.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:09:05.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:09:05.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:09:06.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:09:06.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:09:07.096 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:09:07.096 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:09:07.097 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 16:09:07.097 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:09:07.103 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.33 ms, Average NMS time: 0.86 ms, Average inference time: 7.19 ms

2025-09-01 16:09:07.107 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:09:07.194 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:09:07.275 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch440
2025-09-01 16:09:10.177 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 440/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, 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: 3.342e-04, size: 288, ETA: 0:53:17
2025-09-01 16:09:13.167 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 440/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.8, lr: 3.336e-04, size: 256, ETA: 0:53:13
2025-09-01 16:09:16.109 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 440/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.6, lr: 3.330e-04, size: 416, ETA: 0:53:10
2025-09-01 16:09:19.074 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 440/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 3.323e-04, size: 352, ETA: 0:53:07
2025-09-01 16:09:22.032 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 440/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 3.317e-04, size: 288, ETA: 0:53:04
2025-09-01 16:09:24.941 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 440/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.5, lr: 3.311e-04, size: 384, ETA: 0:53:01
2025-09-01 16:09:26.264 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:09:32.398 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:09:33.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:09:33.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6172
2025-09-01 16:09:33.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5250
2025-09-01 16:09:33.922 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3958
2025-09-01 16:09:33.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5127
2025-09-01 16:09:33.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:09:33.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:09:33.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.617
2025-09-01 16:09:33.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-09-01 16:09:33.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-09-01 16:09:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-09-01 16:09:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:09:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:09:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:09:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:09:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:09:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:09:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:09:33.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:09:33.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:09:34.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:09:35.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:09:35.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:09:36.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:09:37.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:09:37.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:09:38.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:09:39.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:09:39.871 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:09:39.871 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:09:39.871 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:09:39.871 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:09:39.878 | 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

2025-09-01 16:09:39.879 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:09:39.966 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:09:40.050 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch441
2025-09-01 16:09:42.984 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 441/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.7, lr: 3.303e-04, size: 256, ETA: 0:52:56
2025-09-01 16:09:46.154 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 441/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, 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: 3.297e-04, size: 288, ETA: 0:52:53
2025-09-01 16:09:49.049 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 441/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, 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: 3.291e-04, size: 256, ETA: 0:52:50
2025-09-01 16:09:52.257 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 441/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 3.285e-04, size: 512, ETA: 0:52:47
2025-09-01 16:09:55.332 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 441/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.8, iou_loss: 1.8, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.6, lr: 3.279e-04, size: 576, ETA: 0:52:44
2025-09-01 16:09:58.416 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 441/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 3.273e-04, size: 544, ETA: 0:52:41
2025-09-01 16:09:59.772 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:10:06.060 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:10:07.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:10:08.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5829
2025-09-01 16:10:08.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5199
2025-09-01 16:10:08.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3780
2025-09-01 16:10:08.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4936
2025-09-01 16:10:08.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:10:08.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:10:08.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.583
2025-09-01 16:10:08.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-09-01 16:10:08.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-09-01 16:10:08.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-09-01 16:10:08.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:10:08.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:10:08.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:10:08.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:10:08.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:10:08.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:10:08.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:10:08.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:10:08.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:10:10.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:10:11.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:10:12.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:10:14.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:10:15.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:10:16.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:10:17.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:10:19.199 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:10:20.495 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:10:20.496 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 16:10:20.496 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 16:10:20.496 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:10:20.504 | 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: 7.21 ms

2025-09-01 16:10:20.505 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:10:20.586 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:10:20.712 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch442
2025-09-01 16:10:23.530 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 442/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.140s, 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: 3.264e-04, size: 320, ETA: 0:52:36
2025-09-01 16:10:26.593 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 442/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 3.258e-04, size: 416, ETA: 0:52:33
2025-09-01 16:10:29.585 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 442/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 3.252e-04, size: 320, ETA: 0:52:30
2025-09-01 16:10:32.615 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 442/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.9, lr: 3.246e-04, size: 288, ETA: 0:52:27
2025-09-01 16:10:35.692 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 442/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, 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: 3.240e-04, size: 448, ETA: 0:52:24
2025-09-01 16:10:38.699 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 442/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 3.234e-04, size: 512, ETA: 0:52:21
2025-09-01 16:10:40.139 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:10:46.349 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:10:47.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:10:47.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6091
2025-09-01 16:10:47.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5174
2025-09-01 16:10:47.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3852
2025-09-01 16:10:47.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5039
2025-09-01 16:10:47.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:10:47.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:10:47.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-09-01 16:10:47.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 16:10:47.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-09-01 16:10:47.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-09-01 16:10:47.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:10:47.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:10:47.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:10:47.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:10:47.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:10:47.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:10:47.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:10:47.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:10:47.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:10:48.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:10:49.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:10:50.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:10:50.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:10:51.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:10:52.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:10:52.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:10:53.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:10:54.158 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:10:54.158 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:10:54.158 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:10:54.158 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:10:54.165 | 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

2025-09-01 16:10:54.167 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:10:54.252 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:10:54.338 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch443
2025-09-01 16:10:57.322 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 443/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 3.225e-04, size: 320, ETA: 0:52:16
2025-09-01 16:11:00.491 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 443/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 3.219e-04, size: 320, ETA: 0:52:13
2025-09-01 16:11:03.665 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 443/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.4, cls_loss: 0.7, lr: 3.213e-04, size: 576, ETA: 0:52:10
2025-09-01 16:11:06.757 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 443/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.6, lr: 3.207e-04, size: 448, ETA: 0:52:07
2025-09-01 16:11:09.838 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 443/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 3.8, cls_loss: 0.6, lr: 3.201e-04, size: 448, ETA: 0:52:04
2025-09-01 16:11:12.998 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 443/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 1.4, lr: 3.195e-04, size: 544, ETA: 0:52:01
2025-09-01 16:11:14.377 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:11:20.727 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:11:21.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:11:22.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5997
2025-09-01 16:11:22.566 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4893
2025-09-01 16:11:22.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4005
2025-09-01 16:11:22.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4965
2025-09-01 16:11:22.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:11:22.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:11:22.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-09-01 16:11:22.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.489
2025-09-01 16:11:22.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-09-01 16:11:22.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-09-01 16:11:22.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:11:22.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:11:22.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:11:22.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:11:22.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:11:22.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:11:22.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:11:22.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:11:22.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:11:23.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:11:24.291 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:11:25.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:11:25.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:11:26.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:11:27.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:11:28.549 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:11:29.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:11:30.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:11:30.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:11:30.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:11:30.250 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:11:30.258 | 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

2025-09-01 16:11:30.259 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:11:30.372 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:11:30.448 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch444
2025-09-01 16:11:33.307 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 444/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.8, lr: 3.187e-04, size: 512, ETA: 0:51:56
2025-09-01 16:11:36.379 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 444/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, 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: 3.181e-04, size: 256, ETA: 0:51:53
2025-09-01 16:11:39.354 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 444/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 3.175e-04, size: 480, ETA: 0:51:50
2025-09-01 16:11:42.398 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 444/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 3.169e-04, size: 480, ETA: 0:51:47
2025-09-01 16:11:45.405 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 444/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, 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: 3.163e-04, size: 544, ETA: 0:51:44
2025-09-01 16:11:48.560 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 444/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 7.5, iou_loss: 2.6, l1_loss: 1.4, conf_loss: 2.7, cls_loss: 0.7, lr: 3.157e-04, size: 512, ETA: 0:51:41
2025-09-01 16:11:49.927 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:11:56.100 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:11:56.761 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:11:57.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5963
2025-09-01 16:11:57.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5410
2025-09-01 16:11:57.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3724
2025-09-01 16:11:57.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5032
2025-09-01 16:11:57.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:11:57.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:11:57.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.596
2025-09-01 16:11:57.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-09-01 16:11:57.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.372
2025-09-01 16:11:57.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-09-01 16:11:57.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:11:57.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:11:57.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:11:57.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:11:57.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:11:57.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:11:57.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:11:57.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:11:57.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:11:57.864 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:11:58.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:11:59.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:11:59.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:12:00.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:12:00.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:12:01.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:12:01.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:12:02.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:12:02.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:12:02.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:12:02.233 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:12:02.241 | 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: 7.13 ms

2025-09-01 16:12:02.241 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:12:02.324 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:12:02.410 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch445
2025-09-01 16:12:05.223 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 445/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.8, lr: 3.149e-04, size: 448, ETA: 0:51:36
2025-09-01 16:12:08.234 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 445/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.8, l1_loss: 1.5, conf_loss: 1.8, cls_loss: 0.7, lr: 3.143e-04, size: 544, ETA: 0:51:33
2025-09-01 16:12:11.289 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 445/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 3.137e-04, size: 256, ETA: 0:51:30
2025-09-01 16:12:14.244 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 445/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 8.3, iou_loss: 3.4, l1_loss: 1.5, conf_loss: 2.7, cls_loss: 0.8, lr: 3.131e-04, size: 448, ETA: 0:51:27
2025-09-01 16:12:17.199 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 445/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 3.125e-04, size: 320, ETA: 0:51:24
2025-09-01 16:12:20.270 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 445/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 3.119e-04, size: 416, ETA: 0:51:21
2025-09-01 16:12:21.594 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:12:28.032 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:12:28.945 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:12:29.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6020
2025-09-01 16:12:29.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5272
2025-09-01 16:12:29.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4050
2025-09-01 16:12:29.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5114
2025-09-01 16:12:29.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:12:29.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:12:29.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.602
2025-09-01 16:12:29.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-09-01 16:12:29.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-09-01 16:12:29.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-09-01 16:12:29.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:12:29.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:12:29.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:12:29.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:12:29.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:12:29.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:12:29.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:12:29.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:12:29.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:12:30.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:12:31.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:12:32.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:12:32.979 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:12:33.788 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:12:34.576 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:12:35.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:12:36.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:12:36.995 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:12:36.996 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:12:36.996 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:12:36.997 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:12:37.009 | 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: 7.08 ms

2025-09-01 16:12:37.010 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:12:37.131 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:12:37.231 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch446
2025-09-01 16:12:40.170 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 446/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.5, lr: 3.111e-04, size: 480, ETA: 0:51:16
2025-09-01 16:12:43.254 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 446/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 3.105e-04, size: 480, ETA: 0:51:13
2025-09-01 16:12:46.243 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 446/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.5, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 1.0, lr: 3.099e-04, size: 384, ETA: 0:51:10
2025-09-01 16:12:49.245 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 446/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.5, lr: 3.093e-04, size: 448, ETA: 0:51:07
2025-09-01 16:12:52.307 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 446/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.9, lr: 3.087e-04, size: 576, ETA: 0:51:04
2025-09-01 16:12:55.360 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 446/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 3.081e-04, size: 512, ETA: 0:51:00
2025-09-01 16:12:56.789 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:13:02.874 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:13:03.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:13:04.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6077
2025-09-01 16:13:04.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5288
2025-09-01 16:13:04.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3748
2025-09-01 16:13:04.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5038
2025-09-01 16:13:04.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:13:04.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:13:04.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-09-01 16:13:04.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-09-01 16:13:04.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-09-01 16:13:04.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-09-01 16:13:04.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:13:04.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:13:04.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:13:04.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:13:04.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:13:04.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:13:04.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:13:04.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:13:04.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:13:04.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:13:05.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:13:06.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:13:06.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:13:07.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:13:07.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:13:08.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:13:09.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:13:09.604 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:13:09.604 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:13:09.605 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:13:09.605 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:13:09.613 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.14 ms, Average NMS time: 0.89 ms, Average inference time: 7.04 ms

2025-09-01 16:13:09.614 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:13:09.699 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:13:09.781 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch447
2025-09-01 16:13:12.627 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 447/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 3.073e-04, size: 256, ETA: 0:50:56
2025-09-01 16:13:15.555 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 447/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 3.067e-04, size: 448, ETA: 0:50:53
2025-09-01 16:13:18.664 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 447/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.7, lr: 3.061e-04, size: 384, ETA: 0:50:50
2025-09-01 16:13:21.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 447/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.6, lr: 3.055e-04, size: 352, ETA: 0:50:47
2025-09-01 16:13:24.658 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 447/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, 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: 3.049e-04, size: 576, ETA: 0:50:43
2025-09-01 16:13:27.807 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 447/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, 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: 3.043e-04, size: 512, ETA: 0:50:40
2025-09-01 16:13:29.192 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:13:35.291 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:13:35.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:13:36.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5782
2025-09-01 16:13:36.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5088
2025-09-01 16:13:36.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3618
2025-09-01 16:13:36.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4830
2025-09-01 16:13:36.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:13:36.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:13:36.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.578
2025-09-01 16:13:36.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.509
2025-09-01 16:13:36.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.362
2025-09-01 16:13:36.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.483
2025-09-01 16:13:36.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:13:36.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:13:36.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:13:36.179 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:13:36.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:13:36.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:13:36.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:13:36.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:13:36.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:13:36.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:13:37.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:13:37.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:13:37.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:13:38.278 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:13:38.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:13:39.074 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:13:39.477 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:13:39.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:13:39.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:13:39.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 16:13:39.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:13:39.888 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.86 ms, Average inference time: 7.12 ms

2025-09-01 16:13:39.888 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:13:39.975 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:13:40.102 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch448
2025-09-01 16:13:43.071 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 448/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.8, lr: 3.035e-04, size: 544, ETA: 0:50:36
2025-09-01 16:13:46.172 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 448/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 3.029e-04, size: 256, ETA: 0:50:33
2025-09-01 16:13:49.394 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 448/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.159s, 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: 3.023e-04, size: 288, ETA: 0:50:30
2025-09-01 16:13:52.384 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 448/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 3.018e-04, size: 320, ETA: 0:50:27
2025-09-01 16:13:55.379 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 448/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 31.0, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 31.0, cls_loss: 0.0, lr: 3.012e-04, size: 384, ETA: 0:50:23
2025-09-01 16:13:58.629 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 448/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 3.006e-04, size: 480, ETA: 0:50:20
2025-09-01 16:13:59.992 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:14:06.229 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:14:06.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:14:07.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6014
2025-09-01 16:14:07.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5312
2025-09-01 16:14:07.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3677
2025-09-01 16:14:07.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5001
2025-09-01 16:14:07.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:14:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:14:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.601
2025-09-01 16:14:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-09-01 16:14:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-09-01 16:14:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-09-01 16:14:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:14:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:14:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:14:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:14:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:14:07.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:14:07.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:14:07.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:14:07.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:14:08.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:14:08.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:14:09.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:14:09.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:14:10.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:14:11.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:14:11.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:14:12.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:14:12.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:14:12.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:14:12.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:14:12.880 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:14:12.887 | 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

2025-09-01 16:14:12.888 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:14:12.973 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:14:13.055 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch449
2025-09-01 16:14:15.923 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 449/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.7, lr: 2.998e-04, size: 352, ETA: 0:50:16
2025-09-01 16:14:18.931 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 449/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 3.0, cls_loss: 0.7, lr: 2.992e-04, size: 544, ETA: 0:50:13
2025-09-01 16:14:21.953 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 449/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, 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: 2.986e-04, size: 288, ETA: 0:50:10
2025-09-01 16:14:25.116 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 449/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.9, lr: 2.980e-04, size: 384, ETA: 0:50:07
2025-09-01 16:14:28.095 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 449/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 2.974e-04, size: 512, ETA: 0:50:03
2025-09-01 16:14:31.125 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 449/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 1.3, lr: 2.969e-04, size: 256, ETA: 0:50:00
2025-09-01 16:14:32.507 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:14:38.809 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:14:39.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:14:40.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6001
2025-09-01 16:14:40.463 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5171
2025-09-01 16:14:40.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3800
2025-09-01 16:14:40.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4991
2025-09-01 16:14:40.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:14:40.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:14:40.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-09-01 16:14:40.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 16:14:40.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-09-01 16:14:40.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.499
2025-09-01 16:14:40.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:14:40.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:14:40.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:14:40.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:14:40.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:14:40.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:14:40.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:14:40.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:14:40.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:14:41.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:14:42.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:14:42.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:14:43.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:14:44.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:14:45.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:14:45.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:14:46.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:14:47.441 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:14:47.442 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:14:47.442 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:14:47.442 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:14:47.449 | 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

2025-09-01 16:14:47.450 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:14:47.532 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:14:47.625 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch450
2025-09-01 16:14:50.706 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 450/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.6, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.8, lr: 2.960e-04, size: 256, ETA: 0:49:56
2025-09-01 16:14:53.748 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 450/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 2.955e-04, size: 480, ETA: 0:49:53
2025-09-01 16:14:56.855 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 450/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 2.949e-04, size: 288, ETA: 0:49:50
2025-09-01 16:14:59.830 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 450/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 2.943e-04, size: 288, ETA: 0:49:47
2025-09-01 16:15:02.861 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 450/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, 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: 2.937e-04, size: 448, ETA: 0:49:43
2025-09-01 16:15:05.982 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 450/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 2.932e-04, size: 320, ETA: 0:49:40
2025-09-01 16:15:07.332 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:15:13.720 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:15:14.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:15:15.810 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6072
2025-09-01 16:15:15.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5170
2025-09-01 16:15:16.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3628
2025-09-01 16:15:16.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4957
2025-09-01 16:15:16.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:15:16.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:15:16.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-09-01 16:15:16.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 16:15:16.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-09-01 16:15:16.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.496
2025-09-01 16:15:16.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:15:16.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:15:16.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:15:16.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:15:16.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:15:16.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:15:16.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:15:16.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:15:16.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:15:16.997 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:15:18.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:15:18.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:15:19.982 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:15:20.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:15:21.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:15:22.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:15:23.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:15:24.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:15:24.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:15:24.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:15:24.892 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:15:24.899 | 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: 7.21 ms

2025-09-01 16:15:24.901 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:15:24.986 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:15:25.068 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch451
2025-09-01 16:15:27.896 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 451/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 2.923e-04, size: 512, ETA: 0:49:36
2025-09-01 16:15:31.035 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 451/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, 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: 2.917e-04, size: 448, ETA: 0:49:33
2025-09-01 16:15:34.022 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 451/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 2.912e-04, size: 448, ETA: 0:49:30
2025-09-01 16:15:37.092 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 451/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 2.906e-04, size: 448, ETA: 0:49:26
2025-09-01 16:15:40.125 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 451/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 2.900e-04, size: 416, ETA: 0:49:23
2025-09-01 16:15:43.193 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 451/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 1.9, cls_loss: 0.6, lr: 2.895e-04, size: 512, ETA: 0:49:20
2025-09-01 16:15:44.577 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:15:50.790 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:15:51.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:15:51.782 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5836
2025-09-01 16:15:51.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5095
2025-09-01 16:15:51.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3561
2025-09-01 16:15:51.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4831
2025-09-01 16:15:51.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:15:51.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:15:51.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-09-01 16:15:51.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.510
2025-09-01 16:15:51.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.356
2025-09-01 16:15:51.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.483
2025-09-01 16:15:51.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:15:51.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:15:51.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:15:51.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:15:51.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:15:51.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:15:51.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:15:51.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:15:51.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:15:52.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:15:53.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:15:53.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:15:53.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:15:54.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:15:54.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:15:55.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:15:56.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:15:56.519 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:15:56.520 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 16:15:56.520 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 16:15:56.520 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:15:56.527 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.45 ms, Average NMS time: 0.90 ms, Average inference time: 7.35 ms

2025-09-01 16:15:56.528 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:15:56.609 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:15:56.692 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch452
2025-09-01 16:15:59.506 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 452/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 1.1, lr: 2.886e-04, size: 256, ETA: 0:49:16
2025-09-01 16:16:02.491 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 452/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.5, lr: 2.881e-04, size: 480, ETA: 0:49:13
2025-09-01 16:16:05.649 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 452/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.7, cls_loss: 0.7, lr: 2.875e-04, size: 480, ETA: 0:49:10
2025-09-01 16:16:08.778 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 452/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.154s, 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: 2.869e-04, size: 544, ETA: 0:49:06
2025-09-01 16:16:11.884 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 452/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.5, lr: 2.864e-04, size: 544, ETA: 0:49:03
2025-09-01 16:16:14.854 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 452/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.5, lr: 2.858e-04, size: 384, ETA: 0:49:00
2025-09-01 16:16:16.223 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:16:22.544 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:16:23.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:16:23.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6056
2025-09-01 16:16:23.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5208
2025-09-01 16:16:23.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3800
2025-09-01 16:16:24.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5021
2025-09-01 16:16:24.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:16:24.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:16:24.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.606
2025-09-01 16:16:24.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 16:16:24.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-09-01 16:16:24.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 16:16:24.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:16:24.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:16:24.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:16:24.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:16:24.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:16:24.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:16:24.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:16:24.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:16:24.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:16:24.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:16:25.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:16:25.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:16:26.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:16:27.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:16:27.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:16:28.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:16:29.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:16:29.965 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:16:29.966 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:16:29.966 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:16:29.966 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:16:29.973 | 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

2025-09-01 16:16:29.974 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:16:30.053 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:16:30.139 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch453
2025-09-01 16:16:33.155 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 453/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 1.2, lr: 2.850e-04, size: 320, ETA: 0:48:56
2025-09-01 16:16:36.140 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 453/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 2.844e-04, size: 352, ETA: 0:48:53
2025-09-01 16:16:39.056 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 453/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 2.838e-04, size: 384, ETA: 0:48:49
2025-09-01 16:16:42.072 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 453/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 2.833e-04, size: 544, ETA: 0:48:46
2025-09-01 16:16:45.146 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 453/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.7, lr: 2.827e-04, size: 544, ETA: 0:48:43
2025-09-01 16:16:48.184 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 453/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 2.821e-04, size: 352, ETA: 0:48:40
2025-09-01 16:16:49.596 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:16:55.897 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:16:57.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:16:57.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6157
2025-09-01 16:16:57.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5270
2025-09-01 16:16:57.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3654
2025-09-01 16:16:57.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5027
2025-09-01 16:16:57.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:16:57.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:16:57.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.616
2025-09-01 16:16:57.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-09-01 16:16:57.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-09-01 16:16:57.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-09-01 16:16:57.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:16:57.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:16:57.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:16:57.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:16:57.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:16:57.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:16:57.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:16:57.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:16:57.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:16:58.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:16:59.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:17:00.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:17:01.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:17:02.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:17:03.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:17:04.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:17:05.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:17:06.782 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:17:06.782 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:17:06.782 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:17:06.782 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:17:06.789 | 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: 7.09 ms

2025-09-01 16:17:06.792 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:17:06.875 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:17:06.958 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch454
2025-09-01 16:17:09.909 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 454/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 3.6, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.5, lr: 2.813e-04, size: 320, ETA: 0:48:36
2025-09-01 16:17:12.913 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 454/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, 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: 2.807e-04, size: 384, ETA: 0:48:33
2025-09-01 16:17:15.927 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 454/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 3.0, iou_loss: 1.3, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.5, lr: 2.802e-04, size: 320, ETA: 0:48:29
2025-09-01 16:17:18.872 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 454/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 2.796e-04, size: 320, ETA: 0:48:26
2025-09-01 16:17:21.784 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 454/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 2.791e-04, size: 256, ETA: 0:48:23
2025-09-01 16:17:24.843 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 454/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 2.785e-04, size: 512, ETA: 0:48:20
2025-09-01 16:17:26.191 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:17:32.399 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:17:33.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:17:33.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6111
2025-09-01 16:17:33.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5289
2025-09-01 16:17:33.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4063
2025-09-01 16:17:33.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5154
2025-09-01 16:17:33.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:17:33.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:17:33.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.611
2025-09-01 16:17:33.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-09-01 16:17:33.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-09-01 16:17:33.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.515
2025-09-01 16:17:33.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:17:33.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:17:33.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:17:33.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:17:33.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:17:33.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:17:33.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:17:33.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:17:33.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:17:34.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:17:35.369 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:17:36.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:17:36.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:17:37.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:17:38.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:17:38.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:17:39.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:17:40.141 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:17:40.142 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:17:40.142 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 16:17:40.142 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:17:40.149 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.31 ms, Average NMS time: 0.91 ms, Average inference time: 7.22 ms

2025-09-01 16:17:40.151 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:17:40.234 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:17:40.317 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch455
2025-09-01 16:17:43.217 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 455/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 2.777e-04, size: 320, ETA: 0:48:15
2025-09-01 16:17:46.179 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 455/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.9, lr: 2.771e-04, size: 384, ETA: 0:48:12
2025-09-01 16:17:49.113 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 455/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.7, lr: 2.766e-04, size: 416, ETA: 0:48:09
2025-09-01 16:17:52.146 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 455/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 1.0, lr: 2.760e-04, size: 288, ETA: 0:48:06
2025-09-01 16:17:55.181 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 455/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 1.9, cls_loss: 0.8, lr: 2.754e-04, size: 576, ETA: 0:48:03
2025-09-01 16:17:58.169 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 455/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.1, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.4, lr: 2.749e-04, size: 416, ETA: 0:48:00
2025-09-01 16:17:59.490 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:18:05.781 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:18:07.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:18:08.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6010
2025-09-01 16:18:08.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5198
2025-09-01 16:18:08.617 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3867
2025-09-01 16:18:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5025
2025-09-01 16:18:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:18:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:18:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.601
2025-09-01 16:18:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-09-01 16:18:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-09-01 16:18:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 16:18:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:18:08.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:18:08.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:18:08.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:18:08.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:18:08.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:18:08.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:18:08.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:18:08.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:18:09.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:18:11.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:18:12.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:18:13.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:18:15.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:18:16.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:18:17.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:18:19.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:18:20.617 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:18:20.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 16:18:20.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:18:20.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:18:20.625 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.98 ms, Average inference time: 7.21 ms

2025-09-01 16:18:20.626 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:18:20.706 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:18:20.790 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch456
2025-09-01 16:18:23.648 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 456/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 2.741e-04, size: 384, ETA: 0:47:55
2025-09-01 16:18:26.796 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 456/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.0, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.8, lr: 2.735e-04, size: 544, ETA: 0:47:52
2025-09-01 16:18:29.858 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 456/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 2.730e-04, size: 352, ETA: 0:47:49
2025-09-01 16:18:32.943 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 456/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.5, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.5, lr: 2.724e-04, size: 480, ETA: 0:47:46
2025-09-01 16:18:36.011 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 456/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 1.7, cls_loss: 0.6, lr: 2.718e-04, size: 480, ETA: 0:47:43
2025-09-01 16:18:38.994 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 456/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 2.713e-04, size: 352, ETA: 0:47:40
2025-09-01 16:18:40.303 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:18:46.510 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:18:47.457 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:18:48.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6094
2025-09-01 16:18:48.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5351
2025-09-01 16:18:48.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3969
2025-09-01 16:18:48.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5138
2025-09-01 16:18:48.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:18:48.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:18:48.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-09-01 16:18:48.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-09-01 16:18:48.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-09-01 16:18:48.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-09-01 16:18:48.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:18:48.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:18:48.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:18:48.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:18:48.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:18:48.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:18:48.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:18:48.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:18:48.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:18:49.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:18:50.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:18:50.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:18:51.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:18:52.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:18:53.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:18:54.127 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:18:54.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:18:55.809 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:18:55.810 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.28
2025-09-01 16:18:55.810 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:18:55.810 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:18:55.817 | 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

2025-09-01 16:18:55.818 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:18:55.908 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:18:55.989 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch457
2025-09-01 16:18:58.989 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 457/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.6, lr: 2.705e-04, size: 576, ETA: 0:47:35
2025-09-01 16:19:02.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 457/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.163s, data_time: 0.004s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.6, lr: 2.699e-04, size: 256, ETA: 0:47:32
2025-09-01 16:19:05.586 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 457/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 2.2, cls_loss: 1.1, lr: 2.694e-04, size: 288, ETA: 0:47:29
2025-09-01 16:19:08.619 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 457/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, 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: 2.688e-04, size: 320, ETA: 0:47:26
2025-09-01 16:19:11.794 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 457/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 2.683e-04, size: 416, ETA: 0:47:23
2025-09-01 16:19:14.910 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 457/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 2.677e-04, size: 576, ETA: 0:47:20
2025-09-01 16:19:16.371 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:19:22.546 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:19:23.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:19:23.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5985
2025-09-01 16:19:23.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5152
2025-09-01 16:19:23.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3949
2025-09-01 16:19:23.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5028
2025-09-01 16:19:23.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:19:23.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:19:23.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-09-01 16:19:23.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.515
2025-09-01 16:19:23.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-09-01 16:19:23.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-09-01 16:19:23.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:19:23.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:19:23.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:19:23.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:19:23.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:19:23.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:19:23.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:19:23.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:19:23.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:19:24.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:19:25.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:19:25.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:19:26.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:19:27.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:19:27.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:19:28.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:19:28.936 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:19:29.550 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:19:29.550 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:19:29.551 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:19:29.551 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:19:29.558 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.34 ms, Average NMS time: 0.92 ms, Average inference time: 7.25 ms

2025-09-01 16:19:29.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:19:29.647 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:19:29.735 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch458
2025-09-01 16:19:32.486 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 458/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.136s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.7, lr: 2.669e-04, size: 352, ETA: 0:47:15
2025-09-01 16:19:35.480 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 458/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 2.664e-04, size: 544, ETA: 0:47:12
2025-09-01 16:19:38.526 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 458/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.5, conf_loss: 2.3, cls_loss: 0.8, lr: 2.658e-04, size: 416, ETA: 0:47:09
2025-09-01 16:19:41.604 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 458/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 2.653e-04, size: 576, ETA: 0:47:06
2025-09-01 16:19:44.810 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 458/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.7, lr: 2.647e-04, size: 256, ETA: 0:47:03
2025-09-01 16:19:47.781 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 458/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.006s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.6, lr: 2.642e-04, size: 512, ETA: 0:47:00
2025-09-01 16:19:49.121 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:19:55.254 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:19:56.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:19:56.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6047
2025-09-01 16:19:56.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5276
2025-09-01 16:19:56.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3774
2025-09-01 16:19:56.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5032
2025-09-01 16:19:56.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:19:56.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:19:56.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.605
2025-09-01 16:19:56.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-09-01 16:19:56.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-09-01 16:19:56.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-09-01 16:19:56.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:19:56.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:19:56.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:19:56.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:19:56.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:19:56.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:19:56.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:19:56.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:19:56.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:19:57.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:19:58.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:19:58.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:19:59.301 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:19:59.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:20:00.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:20:01.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:20:01.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:20:02.482 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:20:02.482 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:20:02.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:20:02.483 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:20:02.490 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.07 ms, Average NMS time: 0.90 ms, Average inference time: 6.97 ms

2025-09-01 16:20:02.498 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:20:02.638 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:20:02.712 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch459
2025-09-01 16:20:05.668 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 459/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 1.2, lr: 2.634e-04, size: 448, ETA: 0:46:55
2025-09-01 16:20:08.663 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 459/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 3.8, cls_loss: 0.6, lr: 2.628e-04, size: 576, ETA: 0:46:52
2025-09-01 16:20:11.737 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 459/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, 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: 2.623e-04, size: 480, ETA: 0:46:49
2025-09-01 16:20:14.755 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 459/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.7, lr: 2.617e-04, size: 352, ETA: 0:46:46
2025-09-01 16:20:17.878 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 459/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 1.0, cls_loss: 0.5, lr: 2.612e-04, size: 256, ETA: 0:46:43
2025-09-01 16:20:20.809 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 459/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.144s, data_time: 0.003s, total_loss: 8.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 4.6, cls_loss: 0.7, lr: 2.606e-04, size: 544, ETA: 0:46:40
2025-09-01 16:20:22.172 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:20:28.354 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:20:29.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:20:29.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6162
2025-09-01 16:20:29.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5223
2025-09-01 16:20:29.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3677
2025-09-01 16:20:29.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5021
2025-09-01 16:20:29.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:20:29.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:20:29.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.616
2025-09-01 16:20:29.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-09-01 16:20:29.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-09-01 16:20:29.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 16:20:29.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:20:29.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:20:29.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:20:29.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:20:29.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:20:29.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:20:29.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:20:29.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:20:29.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:20:30.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:20:31.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:20:31.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:20:32.304 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:20:32.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:20:33.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:20:34.227 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:20:34.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:20:35.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:20:35.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:20:35.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:20:35.464 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:20:35.471 | 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

2025-09-01 16:20:35.478 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:20:35.566 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:20:35.647 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch460
2025-09-01 16:20:38.542 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 460/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.8, lr: 2.598e-04, size: 320, ETA: 0:46:35
2025-09-01 16:20:41.518 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 460/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 2.593e-04, size: 416, ETA: 0:46:32
2025-09-01 16:20:44.444 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 460/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 3.1, cls_loss: 0.7, lr: 2.587e-04, size: 448, ETA: 0:46:29
2025-09-01 16:20:47.473 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 460/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.7, iou_loss: 1.7, l1_loss: 0.9, conf_loss: 3.6, cls_loss: 0.5, lr: 2.582e-04, size: 480, ETA: 0:46:26
2025-09-01 16:20:50.538 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 460/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.8, lr: 2.576e-04, size: 352, ETA: 0:46:23
2025-09-01 16:20:53.740 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 460/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.158s, data_time: 0.007s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 2.571e-04, size: 384, ETA: 0:46:20
2025-09-01 16:20:55.039 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:21:01.172 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:21:01.995 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:21:02.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6150
2025-09-01 16:21:02.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5166
2025-09-01 16:21:02.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3815
2025-09-01 16:21:02.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5044
2025-09-01 16:21:02.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:21:02.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:21:02.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.615
2025-09-01 16:21:02.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 16:21:02.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-09-01 16:21:02.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-09-01 16:21:02.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:21:02.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:21:02.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:21:02.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:21:02.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:21:02.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:21:02.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:21:02.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:21:02.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:21:03.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:21:04.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:21:04.875 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:21:05.634 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:21:06.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:21:07.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:21:07.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:21:08.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:21:09.088 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:21:09.088 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:21:09.089 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:21:09.089 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:21:09.096 | 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: 7.22 ms

2025-09-01 16:21:09.097 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:21:09.178 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:21:09.262 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch461
2025-09-01 16:21:12.051 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 461/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.138s, data_time: 0.001s, total_loss: 7.8, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.6, lr: 2.563e-04, size: 288, ETA: 0:46:15
2025-09-01 16:21:15.013 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 461/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 2.558e-04, size: 288, ETA: 0:46:12
2025-09-01 16:21:18.079 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 461/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.5, lr: 2.552e-04, size: 576, ETA: 0:46:09
2025-09-01 16:21:21.228 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 461/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 2.547e-04, size: 448, ETA: 0:46:06
2025-09-01 16:21:24.257 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 461/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.7, lr: 2.541e-04, size: 256, ETA: 0:46:03
2025-09-01 16:21:27.260 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 461/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.5, lr: 2.536e-04, size: 384, ETA: 0:46:00
2025-09-01 16:21:28.690 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:21:34.949 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:21:35.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:21:36.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5840
2025-09-01 16:21:36.464 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5212
2025-09-01 16:21:36.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3817
2025-09-01 16:21:36.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4956
2025-09-01 16:21:36.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:21:36.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:21:36.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.584
2025-09-01 16:21:36.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 16:21:36.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-09-01 16:21:36.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.496
2025-09-01 16:21:36.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:21:36.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:21:36.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:21:36.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:21:36.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:21:36.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:21:36.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:21:36.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:21:36.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:21:37.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:21:37.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:21:38.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:21:39.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:21:40.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:21:40.942 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:21:41.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:21:42.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:21:43.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:21:43.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 16:21:43.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:21:43.127 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:21:43.134 | 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

2025-09-01 16:21:43.137 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:21:43.230 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:21:43.310 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch462
2025-09-01 16:21:46.197 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 462/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.142s, 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: 2.528e-04, size: 480, ETA: 0:45:55
2025-09-01 16:21:49.189 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 462/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.5, cls_loss: 0.6, lr: 2.523e-04, size: 480, ETA: 0:45:52
2025-09-01 16:21:52.131 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 462/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 2.517e-04, size: 256, ETA: 0:45:49
2025-09-01 16:21:55.128 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 462/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 4.2, cls_loss: 0.9, lr: 2.512e-04, size: 288, ETA: 0:45:46
2025-09-01 16:21:58.176 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 462/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, 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: 2.507e-04, size: 512, ETA: 0:45:43
2025-09-01 16:22:01.246 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 462/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 2.501e-04, size: 352, ETA: 0:45:40
2025-09-01 16:22:02.650 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:22:08.800 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:22:09.675 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:22:10.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6055
2025-09-01 16:22:10.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5348
2025-09-01 16:22:10.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3784
2025-09-01 16:22:10.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5062
2025-09-01 16:22:10.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:22:10.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:22:10.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.606
2025-09-01 16:22:10.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-09-01 16:22:10.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-09-01 16:22:10.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.506
2025-09-01 16:22:10.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:22:10.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:22:10.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:22:10.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:22:10.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:22:10.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:22:10.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:22:10.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:22:10.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:22:11.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:22:11.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:22:12.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:22:13.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:22:14.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:22:14.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:22:15.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:22:16.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:22:17.028 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:22:17.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:22:17.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:22:17.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:22:17.036 | 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

2025-09-01 16:22:17.037 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:22:17.128 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:22:17.208 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch463
2025-09-01 16:22:20.040 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 463/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.8, lr: 2.494e-04, size: 352, ETA: 0:45:35
2025-09-01 16:22:23.125 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 463/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.7, cls_loss: 0.7, lr: 2.488e-04, size: 512, ETA: 0:45:32
2025-09-01 16:22:26.328 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 463/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 2.483e-04, size: 576, ETA: 0:45:29
2025-09-01 16:22:29.418 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 463/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 2.477e-04, size: 512, ETA: 0:45:26
2025-09-01 16:22:32.475 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 463/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.6, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.5, lr: 2.472e-04, size: 576, ETA: 0:45:23
2025-09-01 16:22:35.552 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 463/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.8, lr: 2.467e-04, size: 416, ETA: 0:45:20
2025-09-01 16:22:36.948 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:22:43.215 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:22:43.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:22:44.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6171
2025-09-01 16:22:44.331 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5102
2025-09-01 16:22:44.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3747
2025-09-01 16:22:44.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5007
2025-09-01 16:22:44.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:22:44.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:22:44.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.617
2025-09-01 16:22:44.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.510
2025-09-01 16:22:44.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-09-01 16:22:44.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-09-01 16:22:44.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:22:44.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:22:44.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:22:44.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:22:44.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:22:44.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:22:44.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:22:44.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:22:44.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:22:44.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:22:45.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:22:45.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:22:46.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:22:47.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:22:47.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:22:48.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:22:48.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:22:49.214 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:22:49.214 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:22:49.214 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:22:49.214 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:22:49.221 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.86 ms, Average inference time: 7.02 ms

2025-09-01 16:22:49.222 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:22:49.302 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:22:49.387 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch464
2025-09-01 16:22:52.295 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 464/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.2, cls_loss: 0.6, lr: 2.459e-04, size: 416, ETA: 0:45:15
2025-09-01 16:22:55.300 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 464/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.9, lr: 2.454e-04, size: 256, ETA: 0:45:12
2025-09-01 16:22:58.303 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 464/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.7, lr: 2.448e-04, size: 352, ETA: 0:45:09
2025-09-01 16:23:01.415 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 464/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 2.443e-04, size: 448, ETA: 0:45:06
2025-09-01 16:23:04.397 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 464/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, 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: 2.438e-04, size: 384, ETA: 0:45:03
2025-09-01 16:23:07.491 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 464/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, 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: 2.432e-04, size: 416, ETA: 0:45:00
2025-09-01 16:23:08.858 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:23:14.994 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:23:15.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:23:16.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6137
2025-09-01 16:23:16.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5247
2025-09-01 16:23:16.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3923
2025-09-01 16:23:16.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5102
2025-09-01 16:23:16.467 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:23:16.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:23:16.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.614
2025-09-01 16:23:16.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-09-01 16:23:16.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-09-01 16:23:16.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.510
2025-09-01 16:23:16.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:23:16.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:23:16.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:23:16.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:23:16.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:23:16.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:23:16.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:23:16.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:23:16.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:23:17.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:23:17.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:23:18.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:23:19.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:23:19.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:23:20.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:23:21.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:23:21.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:23:22.416 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:23:22.416 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:23:22.416 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:23:22.416 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:23:22.423 | 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

2025-09-01 16:23:22.424 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:23:22.514 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:23:22.596 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch465
2025-09-01 16:23:25.505 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 465/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.001s, 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: 512, ETA: 0:44:55
2025-09-01 16:23:28.603 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 465/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 2.419e-04, size: 320, ETA: 0:44:52
2025-09-01 16:23:31.780 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 465/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 2.414e-04, size: 544, ETA: 0:44:49
2025-09-01 16:23:34.862 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 465/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, 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: 2.409e-04, size: 448, ETA: 0:44:46
2025-09-01 16:23:37.897 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 465/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.8, lr: 2.404e-04, size: 512, ETA: 0:44:43
2025-09-01 16:23:40.941 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 465/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 7.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.9, lr: 2.398e-04, size: 384, ETA: 0:44:40
2025-09-01 16:23:42.314 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:23:48.474 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:23:49.427 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:23:50.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6131
2025-09-01 16:23:50.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5343
2025-09-01 16:23:50.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3783
2025-09-01 16:23:50.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5086
2025-09-01 16:23:50.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:23:50.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:23:50.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.613
2025-09-01 16:23:50.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.534
2025-09-01 16:23:50.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-09-01 16:23:50.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.509
2025-09-01 16:23:50.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:23:50.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:23:50.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:23:50.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:23:50.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:23:50.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:23:50.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:23:50.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:23:50.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:23:50.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:23:51.770 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:23:52.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:23:53.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:23:54.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:23:54.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:23:55.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:23:56.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:23:57.034 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:23:57.034 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:23:57.035 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:23:57.035 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:23:57.042 | 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

2025-09-01 16:23:57.043 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:23:57.129 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:23:57.210 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch466
2025-09-01 16:24:00.103 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 466/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 2.391e-04, size: 352, ETA: 0:44:35
2025-09-01 16:24:03.133 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 466/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, 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: 2.385e-04, size: 544, ETA: 0:44:32
2025-09-01 16:24:06.315 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 466/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 2.380e-04, size: 448, ETA: 0:44:29
2025-09-01 16:24:09.386 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 466/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 9.2, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 2.3, lr: 2.375e-04, size: 544, ETA: 0:44:26
2025-09-01 16:24:12.447 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 466/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.8, lr: 2.370e-04, size: 256, ETA: 0:44:23
2025-09-01 16:24:15.508 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 466/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 3.8, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 1.4, cls_loss: 0.5, lr: 2.364e-04, size: 416, ETA: 0:44:20
2025-09-01 16:24:16.836 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:24:23.098 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:24:23.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:24:24.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6083
2025-09-01 16:24:24.470 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5244
2025-09-01 16:24:24.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3881
2025-09-01 16:24:24.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5069
2025-09-01 16:24:24.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:24:24.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:24:24.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-09-01 16:24:24.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-09-01 16:24:24.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-09-01 16:24:24.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.507
2025-09-01 16:24:24.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:24:24.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:24:24.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:24:24.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:24:24.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:24:24.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:24:24.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:24:24.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:24:24.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:24:25.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:24:25.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:24:26.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:24:27.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:24:27.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:24:28.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:24:28.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:24:29.639 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:24:30.261 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:24:30.262 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:24:30.262 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:24:30.262 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:24:30.269 | 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: 7.19 ms

2025-09-01 16:24:30.280 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:24:30.362 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:24:30.444 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch467
2025-09-01 16:24:33.305 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 467/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 2.357e-04, size: 320, ETA: 0:44:15
2025-09-01 16:24:36.369 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 467/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 2.352e-04, size: 480, ETA: 0:44:12
2025-09-01 16:24:39.416 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 467/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.4, cls_loss: 0.7, lr: 2.346e-04, size: 448, ETA: 0:44:09
2025-09-01 16:24:42.430 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 467/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.7, lr: 2.341e-04, size: 544, ETA: 0:44:06
2025-09-01 16:24:45.460 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 467/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 3.7, cls_loss: 0.8, lr: 2.336e-04, size: 288, ETA: 0:44:03
2025-09-01 16:24:48.388 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 467/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 2.331e-04, size: 288, ETA: 0:44:00
2025-09-01 16:24:49.723 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:24:55.879 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:24:56.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:24:57.443 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6080
2025-09-01 16:24:57.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5237
2025-09-01 16:24:57.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3713
2025-09-01 16:24:57.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5010
2025-09-01 16:24:57.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:24:57.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:24:57.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-09-01 16:24:57.629 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-09-01 16:24:57.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-09-01 16:24:57.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.501
2025-09-01 16:24:57.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:24:57.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:24:57.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:24:57.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:24:57.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:24:57.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:24:57.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:24:57.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:24:57.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:24:58.458 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:24:59.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:24:59.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:25:00.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:25:01.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:25:02.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:25:02.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:25:03.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:25:04.278 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:25:04.279 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:25:04.279 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:25:04.279 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:25:04.286 | 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

2025-09-01 16:25:04.287 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:25:04.400 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:25:04.476 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch468
2025-09-01 16:25:07.366 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 468/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.5, cls_loss: 0.5, lr: 2.323e-04, size: 480, ETA: 0:43:55
2025-09-01 16:25:10.448 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 468/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.8, lr: 2.318e-04, size: 288, ETA: 0:43:52
2025-09-01 16:25:13.502 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 468/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.9, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 3.3, cls_loss: 1.1, lr: 2.313e-04, size: 480, ETA: 0:43:49
2025-09-01 16:25:16.484 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 468/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 2.308e-04, size: 512, ETA: 0:43:46
2025-09-01 16:25:19.583 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 468/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 2.302e-04, size: 544, ETA: 0:43:43
2025-09-01 16:25:22.627 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 468/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.9, lr: 2.297e-04, size: 352, ETA: 0:43:40
2025-09-01 16:25:23.971 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:25:30.241 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:25:31.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:25:31.822 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6133
2025-09-01 16:25:31.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5266
2025-09-01 16:25:32.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3945
2025-09-01 16:25:32.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5115
2025-09-01 16:25:32.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:25:32.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:25:32.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.613
2025-09-01 16:25:32.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-09-01 16:25:32.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-09-01 16:25:32.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-09-01 16:25:32.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:25:32.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:25:32.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:25:32.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:25:32.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:25:32.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:25:32.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:25:32.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:25:32.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:25:32.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:25:33.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:25:34.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:25:35.439 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:25:36.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:25:37.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:25:37.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:25:38.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:25:39.677 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:25:39.677 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:25:39.677 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:25:39.678 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:25:39.685 | 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

2025-09-01 16:25:39.686 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:25:39.774 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:25:39.856 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch469
2025-09-01 16:25:42.700 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 469/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 2.1, cls_loss: 0.6, lr: 2.290e-04, size: 480, ETA: 0:43:35
2025-09-01 16:25:45.756 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 469/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, 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: 0.7, lr: 2.285e-04, size: 576, ETA: 0:43:32
2025-09-01 16:25:48.809 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 469/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.6, lr: 2.279e-04, size: 416, ETA: 0:43:29
2025-09-01 16:25:51.896 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 469/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, 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: 2.274e-04, size: 448, ETA: 0:43:26
2025-09-01 16:25:54.869 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 469/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.6, lr: 2.269e-04, size: 480, ETA: 0:43:23
2025-09-01 16:25:57.862 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 469/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 9.2, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 4.5, cls_loss: 0.8, lr: 2.264e-04, size: 512, ETA: 0:43:20
2025-09-01 16:25:59.192 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:26:05.496 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:26:06.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:26:07.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6104
2025-09-01 16:26:07.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5211
2025-09-01 16:26:07.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4062
2025-09-01 16:26:07.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5126
2025-09-01 16:26:07.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:26:07.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:26:07.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.610
2025-09-01 16:26:07.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 16:26:07.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-09-01 16:26:07.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-09-01 16:26:07.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:26:07.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:26:07.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:26:07.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:26:07.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:26:07.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:26:07.225 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:26:07.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:26:07.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:26:08.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:26:08.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:26:09.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:26:10.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:26:11.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:26:12.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:26:12.914 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:26:13.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:26:14.534 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:26:14.534 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:26:14.548 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:26:14.548 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:26:14.560 | 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

2025-09-01 16:26:14.561 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:26:14.682 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:26:14.765 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch470
2025-09-01 16:26:17.698 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 470/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.5, lr: 2.256e-04, size: 352, ETA: 0:43:15
2025-09-01 16:26:20.601 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 470/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.143s, 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: 2.251e-04, size: 320, ETA: 0:43:12
2025-09-01 16:26:23.644 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 470/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 2.246e-04, size: 352, ETA: 0:43:09
2025-09-01 16:26:26.571 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 470/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, 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: 2.241e-04, size: 288, ETA: 0:43:06
2025-09-01 16:26:29.526 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 470/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 2.236e-04, size: 480, ETA: 0:43:03
2025-09-01 16:26:32.607 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 470/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 2.231e-04, size: 288, ETA: 0:43:00
2025-09-01 16:26:33.970 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:26:40.155 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:26:41.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:26:41.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6166
2025-09-01 16:26:41.930 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5220
2025-09-01 16:26:41.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3847
2025-09-01 16:26:41.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5078
2025-09-01 16:26:41.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:26:41.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:26:41.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.617
2025-09-01 16:26:41.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-09-01 16:26:41.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-09-01 16:26:41.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.508
2025-09-01 16:26:41.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:26:41.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:26:41.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:26:41.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:26:41.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:26:41.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:26:41.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:26:41.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:26:41.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:26:42.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:26:43.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:26:44.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:26:45.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:26:45.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:26:46.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:26:47.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:26:48.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:26:49.009 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:26:49.009 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:26:49.009 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:26:49.009 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:26:49.017 | 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

2025-09-01 16:26:49.018 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:26:49.140 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:26:49.221 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch471
2025-09-01 16:26:52.110 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 471/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 2.223e-04, size: 512, ETA: 0:42:55
2025-09-01 16:26:55.196 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 471/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.7, lr: 2.218e-04, size: 480, ETA: 0:42:52
2025-09-01 16:26:58.385 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 471/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 0.8, lr: 2.213e-04, size: 576, ETA: 0:42:49
2025-09-01 16:27:01.419 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 471/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.5, lr: 2.208e-04, size: 512, ETA: 0:42:46
2025-09-01 16:27:04.496 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 471/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, 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: 2.203e-04, size: 544, ETA: 0:42:43
2025-09-01 16:27:07.634 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 471/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, 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: 2.198e-04, size: 512, ETA: 0:42:40
2025-09-01 16:27:09.018 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:27:15.061 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:27:15.906 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:27:16.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6144
2025-09-01 16:27:16.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5189
2025-09-01 16:27:16.671 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4009
2025-09-01 16:27:16.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5114
2025-09-01 16:27:16.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:27:16.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:27:16.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.614
2025-09-01 16:27:16.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.519
2025-09-01 16:27:16.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-09-01 16:27:16.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-09-01 16:27:16.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:27:16.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:27:16.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:27:16.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:27:16.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:27:16.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:27:16.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:27:16.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:27:16.674 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:27:17.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:27:18.159 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:27:18.924 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:27:19.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:27:20.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:27:21.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:27:21.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:27:22.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:27:23.419 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:27:23.420 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:27:23.420 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:27:23.420 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:27:23.427 | 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

2025-09-01 16:27:23.428 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:27:23.510 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:27:23.591 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch472
2025-09-01 16:27:26.436 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 472/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.9, lr: 2.191e-04, size: 288, ETA: 0:42:35
2025-09-01 16:27:29.473 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 472/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 2.186e-04, size: 320, ETA: 0:42:32
2025-09-01 16:27:32.472 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 472/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, 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: 2.180e-04, size: 416, ETA: 0:42:29
2025-09-01 16:27:35.505 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 472/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.0, cls_loss: 0.6, lr: 2.175e-04, size: 416, ETA: 0:42:26
2025-09-01 16:27:38.565 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 472/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.8, lr: 2.170e-04, size: 480, ETA: 0:42:23
2025-09-01 16:27:41.545 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 472/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 3.9, cls_loss: 0.8, lr: 2.165e-04, size: 352, ETA: 0:42:20
2025-09-01 16:27:42.890 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:27:49.170 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:27:50.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:27:50.771 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6074
2025-09-01 16:27:50.865 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5215
2025-09-01 16:27:50.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3835
2025-09-01 16:27:50.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5041
2025-09-01 16:27:50.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:27:50.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:27:50.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-09-01 16:27:50.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 16:27:50.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.384
2025-09-01 16:27:50.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-09-01 16:27:50.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:27:50.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:27:50.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:27:50.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:27:50.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:27:50.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:27:50.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:27:50.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:27:50.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:27:51.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:27:52.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:27:53.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:27:54.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:27:54.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:27:55.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:27:56.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:27:57.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:27:58.191 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:27:58.191 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:27:58.191 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:27:58.191 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:27:58.199 | 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

2025-09-01 16:27:58.199 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:27:58.286 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:27:58.368 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch473
2025-09-01 16:28:01.419 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 473/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.5, lr: 2.158e-04, size: 480, ETA: 0:42:15
2025-09-01 16:28:04.450 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 473/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, 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: 2.153e-04, size: 352, ETA: 0:42:12
2025-09-01 16:28:07.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 473/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 2.148e-04, size: 256, ETA: 0:42:09
2025-09-01 16:28:10.424 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 473/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 2.143e-04, size: 544, ETA: 0:42:06
2025-09-01 16:28:13.490 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 473/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 1.3, conf_loss: 1.5, cls_loss: 0.7, lr: 2.138e-04, size: 544, ETA: 0:42:03
2025-09-01 16:28:16.530 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 473/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 2.133e-04, size: 384, ETA: 0:42:00
2025-09-01 16:28:17.830 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:28:24.218 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:28:24.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:28:25.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6038
2025-09-01 16:28:25.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5341
2025-09-01 16:28:25.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3674
2025-09-01 16:28:25.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5018
2025-09-01 16:28:25.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:28:25.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:28:25.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-09-01 16:28:25.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.534
2025-09-01 16:28:25.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.367
2025-09-01 16:28:25.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 16:28:25.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:28:25.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:28:25.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:28:25.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:28:25.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:28:25.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:28:25.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:28:25.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:28:25.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:28:26.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:28:27.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:28:27.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:28:28.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:28:28.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:28:29.472 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:28:30.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:28:30.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:28:31.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:28:31.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:28:31.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:28:31.330 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:28:31.337 | 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

2025-09-01 16:28:31.338 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:28:31.422 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:28:31.502 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch474
2025-09-01 16:28:34.335 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 474/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 4.2, cls_loss: 1.1, lr: 2.126e-04, size: 288, ETA: 0:41:55
2025-09-01 16:28:37.291 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 474/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 2.121e-04, size: 352, ETA: 0:41:52
2025-09-01 16:28:40.327 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 474/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 2.116e-04, size: 384, ETA: 0:41:49
2025-09-01 16:28:43.315 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 474/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, 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: 2.111e-04, size: 512, ETA: 0:41:46
2025-09-01 16:28:46.355 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 474/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.006s, total_loss: 4.3, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 2.0, cls_loss: 0.4, lr: 2.106e-04, size: 320, ETA: 0:41:43
2025-09-01 16:28:49.452 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 474/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.5, lr: 2.101e-04, size: 416, ETA: 0:41:40
2025-09-01 16:28:50.810 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:28:56.931 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:28:57.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:28:58.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6175
2025-09-01 16:28:58.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5335
2025-09-01 16:28:58.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3768
2025-09-01 16:28:58.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5092
2025-09-01 16:28:58.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:28:58.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:28:58.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.617
2025-09-01 16:28:58.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-09-01 16:28:58.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-09-01 16:28:58.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.509
2025-09-01 16:28:58.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:28:58.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:28:58.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:28:58.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:28:58.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:28:58.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:28:58.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:28:58.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:28:58.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:28:59.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:28:59.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:29:00.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:29:01.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:29:01.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:29:02.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:29:03.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:29:03.632 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:29:04.257 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:29:04.257 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:29:04.257 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:29:04.257 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:29:04.264 | 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

2025-09-01 16:29:04.270 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:29:04.353 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:29:04.476 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch475
2025-09-01 16:29:07.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 475/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 8.3, iou_loss: 3.2, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 1.0, lr: 2.093e-04, size: 256, ETA: 0:41:35
2025-09-01 16:29:10.517 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 475/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.5, lr: 2.089e-04, size: 512, ETA: 0:41:32
2025-09-01 16:29:13.544 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 475/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 0.4, conf_loss: 1.6, cls_loss: 1.6, lr: 2.084e-04, size: 256, ETA: 0:41:29
2025-09-01 16:29:16.658 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 475/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 2.079e-04, size: 448, ETA: 0:41:26
2025-09-01 16:29:19.709 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 475/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.7, lr: 2.074e-04, size: 384, ETA: 0:41:23
2025-09-01 16:29:22.696 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 475/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.0, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.6, lr: 2.069e-04, size: 576, ETA: 0:41:20
2025-09-01 16:29:24.182 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:29:30.551 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:29:31.417 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:29:32.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5956
2025-09-01 16:29:32.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4829
2025-09-01 16:29:32.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3576
2025-09-01 16:29:32.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4787
2025-09-01 16:29:32.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:29:32.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:29:32.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.596
2025-09-01 16:29:32.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.483
2025-09-01 16:29:32.241 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.358
2025-09-01 16:29:32.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.479
2025-09-01 16:29:32.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:29:32.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:29:32.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:29:32.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:29:32.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:29:32.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:29:32.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:29:32.242 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:29:32.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:29:33.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:29:33.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:29:34.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:29:35.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:29:36.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:29:36.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:29:37.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:29:38.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:29:39.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:29:39.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.22
2025-09-01 16:29:39.258 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 16:29:39.259 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:29:39.271 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.33 ms, Average NMS time: 0.93 ms, Average inference time: 7.27 ms

2025-09-01 16:29:39.272 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:29:39.391 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:29:39.498 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch476
2025-09-01 16:29:42.315 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 476/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 3.2, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 0.6, cls_loss: 0.5, lr: 2.062e-04, size: 448, ETA: 0:41:15
2025-09-01 16:29:45.371 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 476/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.7, lr: 2.057e-04, size: 320, ETA: 0:41:12
2025-09-01 16:29:48.384 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 476/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 2.052e-04, size: 416, ETA: 0:41:09
2025-09-01 16:29:51.439 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 476/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.9, lr: 2.047e-04, size: 480, ETA: 0:41:06
2025-09-01 16:29:54.532 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 476/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.8, lr: 2.042e-04, size: 320, ETA: 0:41:03
2025-09-01 16:29:57.548 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 476/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, 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: 2.037e-04, size: 576, ETA: 0:41:00
2025-09-01 16:29:59.059 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:30:05.153 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:30:05.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:30:06.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6066
2025-09-01 16:30:06.454 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5160
2025-09-01 16:30:06.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4107
2025-09-01 16:30:06.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5111
2025-09-01 16:30:06.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:30:06.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:30:06.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-09-01 16:30:06.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-09-01 16:30:06.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-09-01 16:30:06.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-09-01 16:30:06.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:30:06.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:30:06.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:30:06.528 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:30:06.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:30:06.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:30:06.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:30:06.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:30:06.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:30:07.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:30:07.774 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:30:08.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:30:09.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:30:09.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:30:10.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:30:10.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:30:11.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:30:12.213 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:30:12.213 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:30:12.213 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:30:12.213 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:30:12.220 | 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: 7.22 ms

2025-09-01 16:30:12.221 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:30:12.348 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:30:12.464 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch477
2025-09-01 16:30:15.503 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 477/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.9, lr: 2.030e-04, size: 352, ETA: 0:40:55
2025-09-01 16:30:18.585 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 477/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 2.0, cls_loss: 0.7, lr: 2.025e-04, size: 320, ETA: 0:40:52
2025-09-01 16:30:21.526 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 477/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.6, lr: 2.020e-04, size: 320, ETA: 0:40:49
2025-09-01 16:30:24.525 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 477/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, 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: 2.015e-04, size: 512, ETA: 0:40:46
2025-09-01 16:30:27.674 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 477/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, 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: 2.010e-04, size: 480, ETA: 0:40:43
2025-09-01 16:30:30.714 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 477/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.8, lr: 2.005e-04, size: 480, ETA: 0:40:40
2025-09-01 16:30:32.113 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:30:38.393 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:30:39.226 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:30:39.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6113
2025-09-01 16:30:39.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5209
2025-09-01 16:30:39.949 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3775
2025-09-01 16:30:39.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5032
2025-09-01 16:30:39.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:30:39.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:30:39.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.611
2025-09-01 16:30:39.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 16:30:39.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.378
2025-09-01 16:30:39.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-09-01 16:30:39.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:30:39.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:30:39.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:30:39.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:30:39.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:30:39.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:30:39.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:30:39.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:30:39.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:30:40.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:30:41.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:30:42.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:30:42.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:30:43.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:30:44.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:30:44.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:30:45.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:30:46.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:30:46.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:30:46.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:30:46.373 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:30:46.379 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.88 ms, Average inference time: 7.11 ms

2025-09-01 16:30:46.380 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:30:46.470 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:30:46.551 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch478
2025-09-01 16:30:49.390 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 478/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 5.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 1.998e-04, size: 288, ETA: 0:40:35
2025-09-01 16:30:52.372 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 478/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.5, lr: 1.993e-04, size: 512, ETA: 0:40:32
2025-09-01 16:30:55.350 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 478/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, 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: 1.989e-04, size: 320, ETA: 0:40:29
2025-09-01 16:30:58.406 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 478/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.6, lr: 1.984e-04, size: 544, ETA: 0:40:26
2025-09-01 16:31:01.428 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 478/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 4.0, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 1.979e-04, size: 320, ETA: 0:40:23
2025-09-01 16:31:04.358 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 478/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.9, lr: 1.974e-04, size: 416, ETA: 0:40:20
2025-09-01 16:31:05.721 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:31:11.919 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:31:12.662 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:31:13.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6070
2025-09-01 16:31:13.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4945
2025-09-01 16:31:13.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4077
2025-09-01 16:31:13.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5031
2025-09-01 16:31:13.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:31:13.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:31:13.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-09-01 16:31:13.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-09-01 16:31:13.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.408
2025-09-01 16:31:13.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-09-01 16:31:13.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:31:13.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:31:13.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:31:13.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:31:13.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:31:13.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:31:13.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:31:13.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:31:13.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:31:13.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:31:14.437 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:31:15.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:31:15.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:31:16.232 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:31:16.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:31:17.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:31:18.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:31:18.642 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:31:18.642 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:31:18.642 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:31:18.642 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:31:18.648 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.33 ms, Average NMS time: 0.90 ms, Average inference time: 7.23 ms

2025-09-01 16:31:18.649 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:31:18.729 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:31:18.813 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch479
2025-09-01 16:31:21.638 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 479/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 1.967e-04, size: 256, ETA: 0:40:15
2025-09-01 16:31:24.795 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 479/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.6, lr: 1.962e-04, size: 544, ETA: 0:40:12
2025-09-01 16:31:27.971 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 479/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.8, lr: 1.957e-04, size: 320, ETA: 0:40:09
2025-09-01 16:31:30.974 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 479/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.953e-04, size: 480, ETA: 0:40:06
2025-09-01 16:31:33.955 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 479/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.6, lr: 1.948e-04, size: 544, ETA: 0:40:03
2025-09-01 16:31:37.037 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 479/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, 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: 1.943e-04, size: 384, ETA: 0:40:00
2025-09-01 16:31:38.400 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:31:44.630 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:31:45.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:31:45.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6099
2025-09-01 16:31:46.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5163
2025-09-01 16:31:46.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4095
2025-09-01 16:31:46.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5119
2025-09-01 16:31:46.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:31:46.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:31:46.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.610
2025-09-01 16:31:46.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-09-01 16:31:46.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-09-01 16:31:46.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.512
2025-09-01 16:31:46.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:31:46.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:31:46.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:31:46.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:31:46.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:31:46.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:31:46.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:31:46.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:31:46.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:31:46.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:31:47.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:31:48.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:31:48.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:31:49.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:31:50.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:31:50.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:31:51.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:31:52.058 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:31:52.059 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:31:52.059 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:31:52.059 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:31:52.066 | 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: 7.08 ms

2025-09-01 16:31:52.067 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:31:52.147 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:31:52.239 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch480
2025-09-01 16:31:55.196 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 480/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.5, lr: 1.936e-04, size: 384, ETA: 0:39:55
2025-09-01 16:31:58.282 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 480/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 1.931e-04, size: 544, ETA: 0:39:52
2025-09-01 16:32:01.452 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 480/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 9.5, iou_loss: 3.0, l1_loss: 1.0, conf_loss: 3.7, cls_loss: 1.8, lr: 1.926e-04, size: 256, ETA: 0:39:49
2025-09-01 16:32:04.312 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 480/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.1, cls_loss: 0.6, lr: 1.922e-04, size: 448, ETA: 0:39:46
2025-09-01 16:32:07.355 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 480/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.7, lr: 1.917e-04, size: 256, ETA: 0:39:43
2025-09-01 16:32:10.350 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 480/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, 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: 1.912e-04, size: 512, ETA: 0:39:40
2025-09-01 16:32:11.774 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:32:17.935 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:32:18.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:32:19.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6093
2025-09-01 16:32:19.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5275
2025-09-01 16:32:19.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3862
2025-09-01 16:32:19.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5077
2025-09-01 16:32:19.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:32:19.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:32:19.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-09-01 16:32:19.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-09-01 16:32:19.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.386
2025-09-01 16:32:19.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.508
2025-09-01 16:32:19.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:32:19.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:32:19.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:32:19.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:32:19.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:32:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:32:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:32:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:32:19.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:32:19.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:32:20.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:32:20.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:32:21.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:32:21.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:32:22.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:32:22.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:32:23.284 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:32:23.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:32:23.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:32:23.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:32:23.837 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:32:23.844 | 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

2025-09-01 16:32:23.849 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:32:23.937 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:32:24.016 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch481
2025-09-01 16:32:26.839 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 481/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 1.2, lr: 1.905e-04, size: 288, ETA: 0:39:35
2025-09-01 16:32:29.824 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 481/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.4, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 1.900e-04, size: 448, ETA: 0:39:32
2025-09-01 16:32:32.844 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 481/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.896e-04, size: 416, ETA: 0:39:29
2025-09-01 16:32:35.898 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 481/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.151s, 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: 1.891e-04, size: 544, ETA: 0:39:26
2025-09-01 16:32:38.946 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 481/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.7, lr: 1.886e-04, size: 416, ETA: 0:39:23
2025-09-01 16:32:41.933 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 481/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.8, lr: 1.881e-04, size: 320, ETA: 0:39:20
2025-09-01 16:32:43.372 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:32:49.444 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:32:50.077 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:32:50.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6043
2025-09-01 16:32:50.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5118
2025-09-01 16:32:50.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3803
2025-09-01 16:32:50.640 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4988
2025-09-01 16:32:50.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:32:50.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:32:50.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-09-01 16:32:50.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-09-01 16:32:50.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-09-01 16:32:50.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.499
2025-09-01 16:32:50.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:32:50.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:32:50.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:32:50.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:32:50.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:32:50.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:32:50.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:32:50.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:32:50.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:32:51.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:32:51.667 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:32:52.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:32:52.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:32:53.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:32:53.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:32:54.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:32:54.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:32:55.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:32:55.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:32:55.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:32:55.180 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:32:55.187 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.90 ms, Average inference time: 7.20 ms

2025-09-01 16:32:55.188 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:32:55.274 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:32:55.357 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch482
2025-09-01 16:32:58.319 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 482/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 1.874e-04, size: 256, ETA: 0:39:15
2025-09-01 16:33:01.480 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 482/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, 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: 1.870e-04, size: 576, ETA: 0:39:12
2025-09-01 16:33:04.491 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 482/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.8, lr: 1.865e-04, size: 384, ETA: 0:39:09
2025-09-01 16:33:07.473 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 482/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 1.860e-04, size: 352, ETA: 0:39:06
2025-09-01 16:33:10.582 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 482/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 1.856e-04, size: 416, ETA: 0:39:03
2025-09-01 16:33:13.559 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 482/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 10.9, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 5.9, cls_loss: 0.9, lr: 1.851e-04, size: 480, ETA: 0:39:00
2025-09-01 16:33:14.965 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:33:21.171 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:33:21.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:33:22.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6208
2025-09-01 16:33:22.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5051
2025-09-01 16:33:22.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3940
2025-09-01 16:33:22.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5066
2025-09-01 16:33:22.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:33:22.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:33:22.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.621
2025-09-01 16:33:22.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-09-01 16:33:22.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-09-01 16:33:22.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.507
2025-09-01 16:33:22.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:33:22.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:33:22.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:33:22.380 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:33:22.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:33:22.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:33:22.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:33:22.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:33:22.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:33:23.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:33:23.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:33:24.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:33:24.749 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:33:25.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:33:25.888 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:33:26.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:33:27.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:33:27.628 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:33:27.628 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:33:27.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:33:27.629 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:33:27.635 | 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

2025-09-01 16:33:27.637 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:33:27.723 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:33:27.803 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch483
2025-09-01 16:33:30.643 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 483/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.7, lr: 1.844e-04, size: 416, ETA: 0:38:55
2025-09-01 16:33:33.583 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 483/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 1.839e-04, size: 448, ETA: 0:38:52
2025-09-01 16:33:36.545 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 483/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.8, lr: 1.835e-04, size: 416, ETA: 0:38:49
2025-09-01 16:33:39.701 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 483/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 9.9, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 5.1, cls_loss: 0.8, lr: 1.830e-04, size: 576, ETA: 0:38:46
2025-09-01 16:33:42.852 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 483/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 4.6, iou_loss: 2.2, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 1.825e-04, size: 448, ETA: 0:38:43
2025-09-01 16:33:45.870 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 483/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 1.1, lr: 1.821e-04, size: 448, ETA: 0:38:40
2025-09-01 16:33:47.204 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:33:53.487 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:33:54.994 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:33:56.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5994
2025-09-01 16:33:56.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4972
2025-09-01 16:33:56.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3480
2025-09-01 16:33:56.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4815
2025-09-01 16:33:56.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:33:56.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:33:56.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-09-01 16:33:56.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.497
2025-09-01 16:33:56.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.348
2025-09-01 16:33:56.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.482
2025-09-01 16:33:56.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:33:56.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:33:56.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:33:56.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:33:56.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:33:56.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:33:56.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:33:56.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:33:56.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:33:57.722 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:33:59.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:34:00.432 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:34:01.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:34:03.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:34:04.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:34:05.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:34:07.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:34:08.526 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:34:08.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 16:34:08.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 16:34:08.527 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:34:08.534 | 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

2025-09-01 16:34:08.535 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:34:08.616 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:34:08.701 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch484
2025-09-01 16:34:11.640 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 484/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.4, conf_loss: 1.8, cls_loss: 0.5, lr: 1.814e-04, size: 416, ETA: 0:38:35
2025-09-01 16:34:14.586 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 484/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 1.809e-04, size: 480, ETA: 0:38:32
2025-09-01 16:34:17.578 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 484/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 1.805e-04, size: 352, ETA: 0:38:29
2025-09-01 16:34:20.480 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 484/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, 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: 1.800e-04, size: 288, ETA: 0:38:26
2025-09-01 16:34:23.464 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 484/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 7.1, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 1.0, lr: 1.795e-04, size: 288, ETA: 0:38:23
2025-09-01 16:34:26.346 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 484/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 1.791e-04, size: 352, ETA: 0:38:20
2025-09-01 16:34:27.700 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:34:33.857 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:34:34.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:34:35.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6126
2025-09-01 16:34:35.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5229
2025-09-01 16:34:35.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3803
2025-09-01 16:34:35.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5052
2025-09-01 16:34:35.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:34:35.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:34:35.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.613
2025-09-01 16:34:35.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-09-01 16:34:35.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.380
2025-09-01 16:34:35.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-09-01 16:34:35.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:34:35.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:34:35.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:34:35.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:34:35.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:34:35.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:34:35.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:34:35.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:34:35.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:34:36.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:34:36.832 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:34:37.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:34:38.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:34:38.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:34:39.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:34:40.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:34:41.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:34:41.737 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:34:41.738 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:34:41.738 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:34:41.738 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:34:41.749 | 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

2025-09-01 16:34:41.749 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:34:41.835 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:34:41.918 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch485
2025-09-01 16:34:44.762 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 485/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.9, lr: 1.784e-04, size: 256, ETA: 0:38:15
2025-09-01 16:34:47.808 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 485/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, 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: 0.7, lr: 1.779e-04, size: 512, ETA: 0:38:12
2025-09-01 16:34:50.755 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 485/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 1.775e-04, size: 288, ETA: 0:38:09
2025-09-01 16:34:53.750 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 485/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 1.770e-04, size: 320, ETA: 0:38:06
2025-09-01 16:34:56.875 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 485/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.765e-04, size: 480, ETA: 0:38:03
2025-09-01 16:34:59.869 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 485/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 8.3, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.7, cls_loss: 0.9, lr: 1.761e-04, size: 288, ETA: 0:38:00
2025-09-01 16:35:01.223 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:35:07.462 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:35:08.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:35:08.755 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6026
2025-09-01 16:35:08.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5195
2025-09-01 16:35:08.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3913
2025-09-01 16:35:08.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5045
2025-09-01 16:35:08.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:35:08.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:35:08.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.603
2025-09-01 16:35:08.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.519
2025-09-01 16:35:08.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-09-01 16:35:08.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-09-01 16:35:08.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:35:08.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:35:08.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:35:08.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:35:08.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:35:08.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:35:08.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:35:08.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:35:08.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:35:09.655 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:35:10.347 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:35:11.014 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:35:11.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:35:12.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:35:13.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:35:13.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:35:14.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:35:15.072 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:35:15.072 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:35:15.073 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:35:15.073 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:35:15.080 | 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

2025-09-01 16:35:15.081 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:35:15.162 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:35:15.249 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch486
2025-09-01 16:35:18.220 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 486/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.8, lr: 1.754e-04, size: 576, ETA: 0:37:55
2025-09-01 16:35:21.349 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 486/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 1.750e-04, size: 320, ETA: 0:37:52
2025-09-01 16:35:24.446 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 486/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 8.8, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 3.7, cls_loss: 0.7, lr: 1.745e-04, size: 352, ETA: 0:37:49
2025-09-01 16:35:27.511 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 486/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, 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: 1.740e-04, size: 480, ETA: 0:37:46
2025-09-01 16:35:30.675 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 486/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.003s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.6, lr: 1.736e-04, size: 480, ETA: 0:37:43
2025-09-01 16:35:33.718 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 486/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.9, lr: 1.731e-04, size: 320, ETA: 0:37:40
2025-09-01 16:35:35.055 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:35:41.335 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:35:42.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:35:43.049 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6161
2025-09-01 16:35:43.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5364
2025-09-01 16:35:43.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3704
2025-09-01 16:35:43.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5076
2025-09-01 16:35:43.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:35:43.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:35:43.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.616
2025-09-01 16:35:43.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-09-01 16:35:43.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-09-01 16:35:43.250 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.508
2025-09-01 16:35:43.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:35:43.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:35:43.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:35:43.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:35:43.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:35:43.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:35:43.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:35:43.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:35:43.251 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:35:44.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:35:45.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:35:45.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:35:46.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:35:47.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:35:48.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:35:49.733 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:35:50.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:35:52.076 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:35:52.077 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:35:52.077 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:35:52.077 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:35:52.086 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.37 ms, Average NMS time: 0.92 ms, Average inference time: 7.29 ms

2025-09-01 16:35:52.087 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:35:52.178 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:35:52.259 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch487
2025-09-01 16:35:55.151 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 487/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 1.725e-04, size: 384, ETA: 0:37:35
2025-09-01 16:35:58.099 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 487/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, 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: 1.720e-04, size: 480, ETA: 0:37:32
2025-09-01 16:36:01.090 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 487/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, 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: 1.716e-04, size: 576, ETA: 0:37:29
2025-09-01 16:36:04.273 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 487/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.157s, 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: 1.711e-04, size: 480, ETA: 0:37:26
2025-09-01 16:36:07.364 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 487/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.9, cls_loss: 0.7, lr: 1.706e-04, size: 288, ETA: 0:37:23
2025-09-01 16:36:10.432 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 487/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.3, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.7, lr: 1.702e-04, size: 256, ETA: 0:37:20
2025-09-01 16:36:11.785 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:36:18.026 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:36:19.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:36:19.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6082
2025-09-01 16:36:19.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5267
2025-09-01 16:36:19.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3791
2025-09-01 16:36:19.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5047
2025-09-01 16:36:19.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:36:19.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:36:19.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-09-01 16:36:19.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-09-01 16:36:19.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.379
2025-09-01 16:36:19.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-09-01 16:36:19.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:36:19.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:36:19.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:36:19.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:36:19.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:36:19.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:36:19.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:36:19.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:36:19.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:36:20.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:36:21.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:36:22.724 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:36:23.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:36:24.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:36:25.468 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:36:26.392 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:36:27.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:36:28.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:36:28.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:36:28.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:36:28.219 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:36:28.226 | 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

2025-09-01 16:36:28.228 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:36:28.310 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:36:28.391 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch488
2025-09-01 16:36:31.179 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 488/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.138s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.5, lr: 1.695e-04, size: 448, ETA: 0:37:15
2025-09-01 16:36:34.127 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 488/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 1.691e-04, size: 384, ETA: 0:37:12
2025-09-01 16:36:37.188 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 488/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 1.0, lr: 1.686e-04, size: 384, ETA: 0:37:09
2025-09-01 16:36:40.202 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 488/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.682e-04, size: 576, ETA: 0:37:06
2025-09-01 16:36:43.297 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 488/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 1.677e-04, size: 480, ETA: 0:37:03
2025-09-01 16:36:46.299 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 488/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.148s, 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: 1.673e-04, size: 480, ETA: 0:37:00
2025-09-01 16:36:47.661 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:36:54.080 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:36:54.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:36:55.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6179
2025-09-01 16:36:55.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5259
2025-09-01 16:36:55.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4117
2025-09-01 16:36:55.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5185
2025-09-01 16:36:55.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:36:55.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:36:55.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.618
2025-09-01 16:36:55.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-09-01 16:36:55.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-09-01 16:36:55.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.518
2025-09-01 16:36:55.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:36:55.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:36:55.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:36:55.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:36:55.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:36:55.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:36:55.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:36:55.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:36:55.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:36:56.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:36:56.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:36:57.613 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:36:58.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:36:58.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:36:59.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:37:00.376 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:37:01.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:37:01.734 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:37:01.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:37:01.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 16:37:01.735 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:37:01.743 | 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

2025-09-01 16:37:01.744 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:37:01.833 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:37:01.915 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch489
2025-09-01 16:37:04.724 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 489/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.139s, 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: 1.666e-04, size: 352, ETA: 0:36:55
2025-09-01 16:37:07.673 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 489/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 1.662e-04, size: 256, ETA: 0:36:52
2025-09-01 16:37:10.817 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 489/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.6, lr: 1.657e-04, size: 384, ETA: 0:36:49
2025-09-01 16:37:13.855 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 489/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.7, lr: 1.653e-04, size: 384, ETA: 0:36:46
2025-09-01 16:37:16.991 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 489/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.006s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.3, conf_loss: 2.4, cls_loss: 0.6, lr: 1.648e-04, size: 480, ETA: 0:36:43
2025-09-01 16:37:20.125 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 489/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 1.644e-04, size: 288, ETA: 0:36:40
2025-09-01 16:37:21.446 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:37:27.557 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:37:28.943 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:37:29.929 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6015
2025-09-01 16:37:30.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5125
2025-09-01 16:37:30.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4069
2025-09-01 16:37:30.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5069
2025-09-01 16:37:30.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:37:30.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:37:30.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.601
2025-09-01 16:37:30.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-09-01 16:37:30.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-09-01 16:37:30.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.507
2025-09-01 16:37:30.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:37:30.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:37:30.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:37:30.169 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:37:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:37:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:37:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:37:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:37:30.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:37:31.456 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:37:32.703 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:37:33.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:37:35.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:37:36.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:37:37.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:37:38.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:37:40.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:37:41.208 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:37:41.209 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:37:41.209 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:37:41.209 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:37:41.216 | 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

2025-09-01 16:37:41.217 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:37:41.300 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:37:41.383 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch490
2025-09-01 16:37:44.409 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 490/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.9, lr: 1.637e-04, size: 288, ETA: 0:36:36
2025-09-01 16:37:47.358 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 490/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.4, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.4, lr: 1.633e-04, size: 384, ETA: 0:36:32
2025-09-01 16:37:50.382 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 490/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.629e-04, size: 576, ETA: 0:36:29
2025-09-01 16:37:53.510 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 490/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.4, conf_loss: 2.4, cls_loss: 0.7, lr: 1.624e-04, size: 512, ETA: 0:36:26
2025-09-01 16:37:56.615 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 490/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.7, lr: 1.620e-04, size: 256, ETA: 0:36:23
2025-09-01 16:37:59.622 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 490/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.1, iou_loss: 3.1, l1_loss: 0.8, conf_loss: 3.5, cls_loss: 0.7, lr: 1.615e-04, size: 480, ETA: 0:36:20
2025-09-01 16:38:00.976 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:38:07.074 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:38:07.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:38:08.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6144
2025-09-01 16:38:08.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5200
2025-09-01 16:38:08.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3992
2025-09-01 16:38:08.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5112
2025-09-01 16:38:08.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:38:08.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:38:08.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.614
2025-09-01 16:38:08.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-09-01 16:38:08.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-09-01 16:38:08.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-09-01 16:38:08.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:38:08.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:38:08.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:38:08.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:38:08.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:38:08.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:38:08.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:38:08.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:38:08.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:38:09.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:38:10.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:38:10.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:38:11.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:38:12.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:38:13.054 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:38:13.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:38:14.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:38:15.248 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:38:15.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:38:15.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:38:15.249 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:38:15.257 | 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

2025-09-01 16:38:15.257 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:38:15.341 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:38:15.423 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch491
2025-09-01 16:38:18.430 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 491/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 1.5, conf_loss: 2.1, cls_loss: 0.7, lr: 1.609e-04, size: 480, ETA: 0:36:16
2025-09-01 16:38:21.436 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 491/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 1.604e-04, size: 480, ETA: 0:36:13
2025-09-01 16:38:24.368 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 491/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.5, cls_loss: 0.5, lr: 1.600e-04, size: 320, ETA: 0:36:09
2025-09-01 16:38:27.534 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 491/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.8, lr: 1.596e-04, size: 544, ETA: 0:36:06
2025-09-01 16:38:30.576 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 491/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.006s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 1.591e-04, size: 288, ETA: 0:36:03
2025-09-01 16:38:33.511 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 491/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.8, lr: 1.587e-04, size: 288, ETA: 0:36:00
2025-09-01 16:38:34.825 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:38:41.082 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:38:42.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:38:42.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6019
2025-09-01 16:38:43.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5177
2025-09-01 16:38:43.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4092
2025-09-01 16:38:43.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5096
2025-09-01 16:38:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:38:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:38:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.602
2025-09-01 16:38:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.518
2025-09-01 16:38:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-09-01 16:38:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.510
2025-09-01 16:38:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:38:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:38:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:38:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:38:43.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:38:43.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:38:43.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:38:43.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:38:43.108 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:38:44.090 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:38:45.000 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:38:45.905 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:38:46.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:38:47.687 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:38:48.565 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:38:49.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:38:50.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:38:51.292 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:38:51.292 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:38:51.292 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:38:51.292 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:38:51.300 | 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: 7.20 ms

2025-09-01 16:38:51.301 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:38:51.390 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:38:51.524 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch492
2025-09-01 16:38:54.433 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 492/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, 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: 1.581e-04, size: 480, ETA: 0:35:56
2025-09-01 16:38:57.412 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 492/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, 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: 1.576e-04, size: 288, ETA: 0:35:53
2025-09-01 16:39:00.656 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 492/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.8, lr: 1.572e-04, size: 256, ETA: 0:35:49
2025-09-01 16:39:03.554 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 492/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 1.567e-04, size: 288, ETA: 0:35:46
2025-09-01 16:39:06.600 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 492/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 1.6, cls_loss: 0.5, lr: 1.563e-04, size: 384, ETA: 0:35:43
2025-09-01 16:39:09.696 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 492/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 1.559e-04, size: 384, ETA: 0:35:40
2025-09-01 16:39:11.061 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:39:17.191 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:39:17.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:39:18.585 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6162
2025-09-01 16:39:18.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5420
2025-09-01 16:39:18.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3813
2025-09-01 16:39:18.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5132
2025-09-01 16:39:18.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:39:18.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:39:18.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.616
2025-09-01 16:39:18.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-09-01 16:39:18.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-09-01 16:39:18.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-09-01 16:39:18.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:39:18.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:39:18.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:39:18.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:39:18.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:39:18.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:39:18.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:39:18.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:39:18.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:39:19.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:39:20.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:39:20.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:39:21.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:39:22.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:39:22.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:39:23.595 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:39:24.276 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:39:24.997 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:39:24.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:39:24.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:39:24.998 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:39:25.005 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.90 ms, Average inference time: 7.19 ms

2025-09-01 16:39:25.006 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:39:25.092 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:39:25.179 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch493
2025-09-01 16:39:28.045 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 493/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 4.4, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.6, lr: 1.552e-04, size: 448, ETA: 0:35:36
2025-09-01 16:39:31.140 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 493/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 4.5, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 1.548e-04, size: 416, ETA: 0:35:33
2025-09-01 16:39:34.169 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 493/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.6, lr: 1.544e-04, size: 544, ETA: 0:35:30
2025-09-01 16:39:37.215 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 493/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 3.0, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 1.539e-04, size: 416, ETA: 0:35:26
2025-09-01 16:39:40.188 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 493/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.8, lr: 1.535e-04, size: 512, ETA: 0:35:23
2025-09-01 16:39:43.233 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 493/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 1.3, conf_loss: 2.3, cls_loss: 0.7, lr: 1.531e-04, size: 544, ETA: 0:35:20
2025-09-01 16:39:44.648 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:39:50.811 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:39:51.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:39:52.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5929
2025-09-01 16:39:52.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5051
2025-09-01 16:39:52.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3643
2025-09-01 16:39:52.315 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4874
2025-09-01 16:39:52.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:39:52.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:39:52.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-09-01 16:39:52.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-09-01 16:39:52.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.364
2025-09-01 16:39:52.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-09-01 16:39:52.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:39:52.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:39:52.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:39:52.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:39:52.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:39:52.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:39:52.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:39:52.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:39:52.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:39:53.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:39:53.737 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:39:54.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:39:55.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:39:55.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:39:56.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:39:57.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:39:57.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:39:58.564 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:39:58.564 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 16:39:58.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 16:39:58.565 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:39:58.572 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.90 ms, Average inference time: 7.19 ms

2025-09-01 16:39:58.573 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:39:58.708 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:39:58.783 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch494
2025-09-01 16:40:01.659 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 494/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 1.524e-04, size: 544, ETA: 0:35:16
2025-09-01 16:40:04.701 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 494/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.8, lr: 1.520e-04, size: 448, ETA: 0:35:13
2025-09-01 16:40:07.794 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 494/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.7, lr: 1.516e-04, size: 352, ETA: 0:35:10
2025-09-01 16:40:10.922 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 494/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 1.0, lr: 1.512e-04, size: 544, ETA: 0:35:07
2025-09-01 16:40:13.958 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 494/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.507e-04, size: 448, ETA: 0:35:03
2025-09-01 16:40:16.912 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 494/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.5, lr: 1.503e-04, size: 320, ETA: 0:35:00
2025-09-01 16:40:18.260 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:40:24.697 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:40:25.818 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:40:26.623 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6056
2025-09-01 16:40:26.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5308
2025-09-01 16:40:26.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3990
2025-09-01 16:40:26.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5118
2025-09-01 16:40:26.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:40:26.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:40:26.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.606
2025-09-01 16:40:26.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-09-01 16:40:26.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-09-01 16:40:26.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.512
2025-09-01 16:40:26.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:40:26.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:40:26.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:40:26.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:40:26.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:40:26.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:40:26.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:40:26.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:40:26.968 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:40:28.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:40:29.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:40:30.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:40:31.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:40:32.005 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:40:33.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:40:34.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:40:35.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:40:36.004 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:40:36.005 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:40:36.005 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:40:36.005 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:40:36.014 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.36 ms, Average NMS time: 0.92 ms, Average inference time: 7.28 ms

2025-09-01 16:40:36.015 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:40:36.103 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:40:36.186 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch495
2025-09-01 16:40:39.122 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 495/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.9, iou_loss: 1.3, l1_loss: 0.4, conf_loss: 1.9, cls_loss: 4.3, lr: 1.497e-04, size: 256, ETA: 0:34:56
2025-09-01 16:40:42.256 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 495/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.7, lr: 1.493e-04, size: 448, ETA: 0:34:53
2025-09-01 16:40:45.267 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 495/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.6, lr: 1.488e-04, size: 384, ETA: 0:34:50
2025-09-01 16:40:48.330 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 495/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.6, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.5, lr: 1.484e-04, size: 576, ETA: 0:34:47
2025-09-01 16:40:51.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 495/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.165s, data_time: 0.006s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.8, lr: 1.480e-04, size: 384, ETA: 0:34:44
2025-09-01 16:40:54.637 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 495/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 1.476e-04, size: 256, ETA: 0:34:40
2025-09-01 16:40:55.992 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:41:02.293 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:41:03.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:41:03.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5891
2025-09-01 16:41:03.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5054
2025-09-01 16:41:03.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3654
2025-09-01 16:41:03.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4866
2025-09-01 16:41:03.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:41:03.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:41:03.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-09-01 16:41:03.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.505
2025-09-01 16:41:03.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.365
2025-09-01 16:41:03.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-09-01 16:41:03.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:41:03.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:41:03.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:41:03.677 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:41:03.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:41:03.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:41:03.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:41:03.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:41:03.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:41:04.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:41:04.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:41:05.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:41:06.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:41:06.837 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:41:07.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:41:08.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:41:08.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:41:09.364 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:41:09.365 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 16:41:09.365 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 16:41:09.365 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:41:09.372 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.33 ms, Average NMS time: 0.87 ms, Average inference time: 7.20 ms

2025-09-01 16:41:09.373 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:41:09.497 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:41:09.572 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch496
2025-09-01 16:41:12.661 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 496/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, 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: 1.469e-04, size: 256, ETA: 0:34:36
2025-09-01 16:41:15.724 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 496/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, 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: 1.465e-04, size: 544, ETA: 0:34:33
2025-09-01 16:41:18.814 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 496/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.8, lr: 1.461e-04, size: 512, ETA: 0:34:30
2025-09-01 16:41:21.908 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 496/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, 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: 1.457e-04, size: 576, ETA: 0:34:27
2025-09-01 16:41:25.166 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 496/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 8.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 1.2, lr: 1.452e-04, size: 256, ETA: 0:34:24
2025-09-01 16:41:28.324 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 496/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 1.0, lr: 1.448e-04, size: 384, ETA: 0:34:21
2025-09-01 16:41:29.663 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:41:35.944 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:41:36.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:41:37.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6087
2025-09-01 16:41:37.487 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5364
2025-09-01 16:41:37.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3871
2025-09-01 16:41:37.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5107
2025-09-01 16:41:37.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:41:37.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:41:37.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-09-01 16:41:37.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-09-01 16:41:37.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-09-01 16:41:37.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-09-01 16:41:37.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:41:37.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:41:37.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:41:37.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:41:37.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:41:37.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:41:37.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:41:37.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:41:37.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:41:38.290 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:41:39.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:41:39.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:41:40.495 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:41:41.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:41:41.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:41:42.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:41:43.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:41:44.195 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:41:44.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:41:44.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:41:44.196 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:41:44.203 | 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

2025-09-01 16:41:44.204 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:41:44.287 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:41:44.379 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch497
2025-09-01 16:41:47.337 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 497/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.442e-04, size: 512, ETA: 0:34:16
2025-09-01 16:41:50.345 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 497/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 1.7, cls_loss: 0.7, lr: 1.438e-04, size: 480, ETA: 0:34:13
2025-09-01 16:41:53.333 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 497/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, 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: 1.434e-04, size: 480, ETA: 0:34:10
2025-09-01 16:41:56.371 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 497/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.6, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.5, lr: 1.430e-04, size: 544, ETA: 0:34:07
2025-09-01 16:41:59.408 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 497/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 1.7, cls_loss: 0.4, lr: 1.425e-04, size: 288, ETA: 0:34:04
2025-09-01 16:42:02.306 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 497/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, 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: 1.421e-04, size: 352, ETA: 0:34:01
2025-09-01 16:42:03.657 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:42:09.896 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:42:11.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:42:11.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6140
2025-09-01 16:42:11.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5261
2025-09-01 16:42:11.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4043
2025-09-01 16:42:11.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5148
2025-09-01 16:42:11.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:42:11.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:42:11.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.614
2025-09-01 16:42:11.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-09-01 16:42:11.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.404
2025-09-01 16:42:11.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.515
2025-09-01 16:42:11.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:42:11.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:42:11.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:42:11.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:42:11.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:42:11.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:42:11.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:42:11.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:42:11.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:42:12.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:42:13.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:42:14.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:42:15.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:42:16.759 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:42:17.735 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:42:18.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:42:19.641 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:42:20.587 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:42:20.587 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:42:20.588 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:42:20.588 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:42:20.595 | 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

2025-09-01 16:42:20.596 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:42:20.676 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:42:20.760 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch498
2025-09-01 16:42:23.663 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 498/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, 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: 1.415e-04, size: 480, ETA: 0:33:56
2025-09-01 16:42:26.646 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 498/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 3.2, iou_loss: 1.2, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.4, lr: 1.411e-04, size: 480, ETA: 0:33:53
2025-09-01 16:42:29.710 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 498/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.4, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 1.407e-04, size: 576, ETA: 0:33:50
2025-09-01 16:42:32.817 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 498/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 1.403e-04, size: 320, ETA: 0:33:47
2025-09-01 16:42:35.904 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 498/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 1.399e-04, size: 544, ETA: 0:33:44
2025-09-01 16:42:38.973 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 498/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.6, lr: 1.394e-04, size: 256, ETA: 0:33:41
2025-09-01 16:42:40.284 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:42:46.602 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:42:47.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:42:48.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6173
2025-09-01 16:42:48.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5305
2025-09-01 16:42:48.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3941
2025-09-01 16:42:48.806 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5140
2025-09-01 16:42:48.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:42:48.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:42:48.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.617
2025-09-01 16:42:48.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-09-01 16:42:48.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-09-01 16:42:48.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-09-01 16:42:48.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:42:48.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:42:48.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:42:48.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:42:48.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:42:48.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:42:48.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:42:48.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:42:48.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:42:49.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:42:50.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:42:51.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:42:52.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:42:53.932 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:42:54.965 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:42:56.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:42:57.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:42:58.032 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:42:58.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:42:58.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:42:58.033 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:42:58.040 | 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

2025-09-01 16:42:58.047 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:42:58.130 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:42:58.221 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch499
2025-09-01 16:43:01.152 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 499/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.6, lr: 1.388e-04, size: 256, ETA: 0:33:36
2025-09-01 16:43:04.233 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 499/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 1.384e-04, size: 512, ETA: 0:33:33
2025-09-01 16:43:07.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 499/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 1.380e-04, size: 448, ETA: 0:33:30
2025-09-01 16:43:10.256 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 499/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.5Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 3.3, cls_loss: 0.6, lr: 1.376e-04, size: 288, ETA: 0:33:27
2025-09-01 16:43:13.263 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 499/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.8, l1_loss: 0.4, conf_loss: 1.1, cls_loss: 0.6, lr: 1.372e-04, size: 288, ETA: 0:33:24
2025-09-01 16:43:16.367 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 499/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.9, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 5.9, cls_loss: 0.0, lr: 1.368e-04, size: 416, ETA: 0:33:21
2025-09-01 16:43:17.718 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:43:24.057 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:43:24.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:43:25.258 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6070
2025-09-01 16:43:25.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5066
2025-09-01 16:43:25.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3822
2025-09-01 16:43:25.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4986
2025-09-01 16:43:25.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:43:25.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:43:25.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-09-01 16:43:25.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.507
2025-09-01 16:43:25.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.382
2025-09-01 16:43:25.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.499
2025-09-01 16:43:25.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:43:25.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:43:25.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:43:25.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:43:25.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:43:25.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:43:25.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:43:25.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:43:25.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:43:26.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:43:26.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:43:27.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:43:27.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:43:28.421 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:43:29.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:43:29.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:43:30.310 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:43:30.895 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:43:30.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:43:30.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:43:30.896 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:43:30.903 | 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

2025-09-01 16:43:30.908 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:43:30.994 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:43:31.074 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch500
2025-09-01 16:43:33.858 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 500/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.138s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.5, lr: 1.362e-04, size: 320, ETA: 0:33:16
2025-09-01 16:43:36.999 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 500/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.8, lr: 1.358e-04, size: 384, ETA: 0:33:13
2025-09-01 16:43:40.001 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 500/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 2.5, iou_loss: 1.1, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.4, lr: 1.354e-04, size: 320, ETA: 0:33:10
2025-09-01 16:43:42.930 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 500/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, 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: 1.350e-04, size: 320, ETA: 0:33:07
2025-09-01 16:43:45.971 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 500/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 9.3, iou_loss: 2.7, l1_loss: 1.7, conf_loss: 3.8, cls_loss: 1.1, lr: 1.346e-04, size: 576, ETA: 0:33:04
2025-09-01 16:43:49.063 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 500/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, 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: 1.342e-04, size: 448, ETA: 0:33:01
2025-09-01 16:43:50.484 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:43:56.635 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:43:57.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:43:58.027 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6061
2025-09-01 16:43:58.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5282
2025-09-01 16:43:58.233 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4065
2025-09-01 16:43:58.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5136
2025-09-01 16:43:58.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:43:58.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:43:58.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.606
2025-09-01 16:43:58.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-09-01 16:43:58.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-09-01 16:43:58.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-09-01 16:43:58.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:43:58.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:43:58.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:43:58.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:43:58.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:43:58.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:43:58.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:43:58.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:43:58.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:43:59.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:43:59.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:44:00.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:44:01.318 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:44:02.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:44:02.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:44:03.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:44:04.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:44:05.066 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:44:05.067 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:44:05.067 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:44:05.067 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:44:05.074 | 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

2025-09-01 16:44:05.075 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:44:05.157 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:44:05.241 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch501
2025-09-01 16:44:08.123 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 501/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.336e-04, size: 416, ETA: 0:32:56
2025-09-01 16:44:11.106 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 501/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.332e-04, size: 416, ETA: 0:32:53
2025-09-01 16:44:14.137 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 501/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 1.328e-04, size: 256, ETA: 0:32:50
2025-09-01 16:44:17.117 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 501/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.0, cls_loss: 0.7, lr: 1.324e-04, size: 480, ETA: 0:32:47
2025-09-01 16:44:20.193 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 501/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, 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: 1.320e-04, size: 256, ETA: 0:32:44
2025-09-01 16:44:23.182 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 501/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, 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: 1.315e-04, size: 416, ETA: 0:32:41
2025-09-01 16:44:24.517 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:44:30.775 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:44:31.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:44:32.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5989
2025-09-01 16:44:32.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4734
2025-09-01 16:44:32.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3873
2025-09-01 16:44:32.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4865
2025-09-01 16:44:32.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:44:32.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:44:32.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-09-01 16:44:32.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.473
2025-09-01 16:44:32.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-09-01 16:44:32.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.487
2025-09-01 16:44:32.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:44:32.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:44:32.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:44:32.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:44:32.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:44:32.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:44:32.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:44:32.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:44:32.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:44:33.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:44:34.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:44:35.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:44:36.478 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:44:37.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:44:38.243 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:44:39.177 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:44:40.138 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:44:41.058 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:44:41.058 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.23
2025-09-01 16:44:41.058 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 16:44:41.058 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:44:41.065 | 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

2025-09-01 16:44:41.070 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:44:41.150 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:44:41.233 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch502
2025-09-01 16:44:44.030 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 502/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.6, lr: 1.310e-04, size: 512, ETA: 0:32:36
2025-09-01 16:44:46.961 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 502/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.6, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 2.2, cls_loss: 0.7, lr: 1.306e-04, size: 352, ETA: 0:32:33
2025-09-01 16:44:50.097 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 502/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 1.302e-04, size: 448, ETA: 0:32:30
2025-09-01 16:44:53.083 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 502/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, 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: 1.298e-04, size: 320, ETA: 0:32:27
2025-09-01 16:44:56.227 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 502/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 1.294e-04, size: 512, ETA: 0:32:24
2025-09-01 16:44:59.335 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 502/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.8, lr: 1.290e-04, size: 288, ETA: 0:32:21
2025-09-01 16:45:00.760 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:45:06.981 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:45:07.701 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:45:08.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6105
2025-09-01 16:45:08.288 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5315
2025-09-01 16:45:08.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3987
2025-09-01 16:45:08.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5136
2025-09-01 16:45:08.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:45:08.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:45:08.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.611
2025-09-01 16:45:08.320 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-09-01 16:45:08.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-09-01 16:45:08.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-09-01 16:45:08.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:45:08.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:45:08.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:45:08.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:45:08.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:45:08.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:45:08.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:45:08.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:45:08.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:45:09.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:45:09.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:45:10.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:45:10.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:45:11.509 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:45:12.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:45:12.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:45:13.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:45:14.003 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:45:14.003 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:45:14.003 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:45:14.003 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:45:14.014 | 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

2025-09-01 16:45:14.019 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:45:14.205 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:45:14.284 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch503
2025-09-01 16:45:17.177 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 503/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, 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: 1.284e-04, size: 288, ETA: 0:32:17
2025-09-01 16:45:20.237 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 503/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 17.8, iou_loss: 3.5, l1_loss: 1.5, conf_loss: 12.0, cls_loss: 0.9, lr: 1.280e-04, size: 544, ETA: 0:32:13
2025-09-01 16:45:23.355 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 503/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 0.9, lr: 1.276e-04, size: 256, ETA: 0:32:10
2025-09-01 16:45:26.286 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 503/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 1.272e-04, size: 320, ETA: 0:32:07
2025-09-01 16:45:29.326 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 503/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.4, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.8, lr: 1.268e-04, size: 320, ETA: 0:32:04
2025-09-01 16:45:32.602 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 503/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.5, lr: 1.264e-04, size: 352, ETA: 0:32:01
2025-09-01 16:45:33.993 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:45:40.128 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:45:40.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:45:41.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5955
2025-09-01 16:45:41.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5224
2025-09-01 16:45:41.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3631
2025-09-01 16:45:41.440 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4936
2025-09-01 16:45:41.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:45:41.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:45:41.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.595
2025-09-01 16:45:41.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-09-01 16:45:41.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.363
2025-09-01 16:45:41.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.494
2025-09-01 16:45:41.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:45:41.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:45:41.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:45:41.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:45:41.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:45:41.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:45:41.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:45:41.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:45:41.442 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:45:42.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:45:42.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:45:43.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:45:43.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:45:44.353 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:45:44.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:45:45.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:45:46.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:45:46.655 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:45:46.655 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 16:45:46.655 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 16:45:46.655 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:45:46.662 | 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

2025-09-01 16:45:46.671 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:45:46.753 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:45:46.839 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch504
2025-09-01 16:45:49.762 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 504/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, 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: 1.258e-04, size: 416, ETA: 0:31:57
2025-09-01 16:45:52.780 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 504/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 1.254e-04, size: 512, ETA: 0:31:54
2025-09-01 16:45:55.846 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 504/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 3.9, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.6, lr: 1.250e-04, size: 288, ETA: 0:31:51
2025-09-01 16:45:58.875 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 504/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.247e-04, size: 576, ETA: 0:31:47
2025-09-01 16:46:01.969 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 504/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.5, lr: 1.243e-04, size: 416, ETA: 0:31:44
2025-09-01 16:46:04.996 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 504/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 1.239e-04, size: 352, ETA: 0:31:41
2025-09-01 16:46:06.319 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:46:12.717 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:46:13.612 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:46:14.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6191
2025-09-01 16:46:14.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5277
2025-09-01 16:46:14.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3875
2025-09-01 16:46:14.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5114
2025-09-01 16:46:14.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:46:14.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:46:14.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.619
2025-09-01 16:46:14.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-09-01 16:46:14.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-09-01 16:46:14.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-09-01 16:46:14.372 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:46:14.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:46:14.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:46:14.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:46:14.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:46:14.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:46:14.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:46:14.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:46:14.373 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:46:15.220 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:46:15.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:46:16.766 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:46:17.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:46:18.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:46:19.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:46:19.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:46:20.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:46:21.364 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:46:21.364 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:46:21.364 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:46:21.364 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:46:21.372 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.43 ms, Average NMS time: 0.90 ms, Average inference time: 7.33 ms

2025-09-01 16:46:21.377 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:46:21.462 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:46:21.590 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch505
2025-09-01 16:46:24.437 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 505/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 3.8, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 0.9, cls_loss: 0.5, lr: 1.233e-04, size: 448, ETA: 0:31:37
2025-09-01 16:46:27.415 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 505/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.8, lr: 1.229e-04, size: 288, ETA: 0:31:34
2025-09-01 16:46:30.510 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 505/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.9, lr: 1.225e-04, size: 384, ETA: 0:31:31
2025-09-01 16:46:33.505 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 505/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.7, lr: 1.221e-04, size: 288, ETA: 0:31:28
2025-09-01 16:46:36.434 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 505/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.003s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 1.217e-04, size: 352, ETA: 0:31:24
2025-09-01 16:46:39.449 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 505/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.2, l1_loss: 0.4, conf_loss: 1.3, cls_loss: 0.5, lr: 1.214e-04, size: 320, ETA: 0:31:21
2025-09-01 16:46:40.776 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:46:46.894 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:46:47.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:46:48.608 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6075
2025-09-01 16:46:48.719 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5134
2025-09-01 16:46:48.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3766
2025-09-01 16:46:48.796 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4991
2025-09-01 16:46:48.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:46:48.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:46:48.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-09-01 16:46:48.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-09-01 16:46:48.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-09-01 16:46:48.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.499
2025-09-01 16:46:48.800 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:46:48.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:46:48.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:46:48.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:46:48.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:46:48.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:46:48.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:46:48.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:46:48.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:46:49.708 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:46:50.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:46:51.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:46:52.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:46:53.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:46:53.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:46:54.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:46:55.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:46:56.537 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:46:56.538 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:46:56.538 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:46:56.538 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:46:56.545 | 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

2025-09-01 16:46:56.546 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:46:56.634 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:46:56.715 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch506
2025-09-01 16:46:59.562 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 506/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 0.6, cls_loss: 0.5, lr: 1.208e-04, size: 320, ETA: 0:31:17
2025-09-01 16:47:02.543 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 506/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 1.3, conf_loss: 3.1, cls_loss: 0.7, lr: 1.204e-04, size: 576, ETA: 0:31:14
2025-09-01 16:47:05.628 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 506/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.200e-04, size: 416, ETA: 0:31:11
2025-09-01 16:47:08.565 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 506/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.5, lr: 1.196e-04, size: 480, ETA: 0:31:08
2025-09-01 16:47:11.854 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 506/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.163s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.6, lr: 1.193e-04, size: 512, ETA: 0:31:05
2025-09-01 16:47:14.900 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 506/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 1.189e-04, size: 480, ETA: 0:31:01
2025-09-01 16:47:16.231 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:47:22.391 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:47:23.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:47:23.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6132
2025-09-01 16:47:23.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5277
2025-09-01 16:47:23.973 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4003
2025-09-01 16:47:23.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5137
2025-09-01 16:47:23.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:47:23.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:47:23.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.613
2025-09-01 16:47:23.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-09-01 16:47:23.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-09-01 16:47:23.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-09-01 16:47:23.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:47:23.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:47:23.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:47:23.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:47:23.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:47:23.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:47:23.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:47:23.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:47:23.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:47:24.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:47:25.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:47:26.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:47:26.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:47:27.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:47:28.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:47:28.925 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:47:29.603 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:47:30.325 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:47:30.326 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:47:30.326 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:47:30.326 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:47:30.333 | 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: 7.13 ms

2025-09-01 16:47:30.335 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:47:30.422 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:47:30.512 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch507
2025-09-01 16:47:33.369 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 507/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.183e-04, size: 512, ETA: 0:30:57
2025-09-01 16:47:36.439 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 507/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, 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: 1.179e-04, size: 352, ETA: 0:30:54
2025-09-01 16:47:39.502 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 507/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, 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: 1.175e-04, size: 320, ETA: 0:30:51
2025-09-01 16:47:42.525 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 507/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.6, lr: 1.172e-04, size: 480, ETA: 0:30:48
2025-09-01 16:47:45.615 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 507/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.8, lr: 1.168e-04, size: 352, ETA: 0:30:45
2025-09-01 16:47:48.551 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 507/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 1.164e-04, size: 480, ETA: 0:30:42
2025-09-01 16:47:49.903 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:47:56.227 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:47:57.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:47:57.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6052
2025-09-01 16:47:58.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5121
2025-09-01 16:47:58.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3811
2025-09-01 16:47:58.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4994
2025-09-01 16:47:58.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:47:58.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:47:58.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.605
2025-09-01 16:47:58.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.512
2025-09-01 16:47:58.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.381
2025-09-01 16:47:58.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.499
2025-09-01 16:47:58.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:47:58.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:47:58.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:47:58.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:47:58.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:47:58.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:47:58.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:47:58.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:47:58.060 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:47:58.887 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:47:59.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:48:00.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:48:01.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:48:02.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:48:03.013 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:48:03.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:48:04.630 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:48:05.466 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:48:05.466 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 16:48:05.466 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:48:05.466 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:48:05.474 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.33 ms, Average NMS time: 0.93 ms, Average inference time: 7.26 ms

2025-09-01 16:48:05.475 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:48:05.553 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:48:05.637 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch508
2025-09-01 16:48:08.569 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 508/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, 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: 1.159e-04, size: 320, ETA: 0:30:37
2025-09-01 16:48:11.712 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 508/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, 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: 1.155e-04, size: 256, ETA: 0:30:34
2025-09-01 16:48:14.744 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 508/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 10.2, iou_loss: 3.4, l1_loss: 1.3, conf_loss: 4.6, cls_loss: 0.9, lr: 1.151e-04, size: 256, ETA: 0:30:31
2025-09-01 16:48:17.868 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 508/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 1.147e-04, size: 256, ETA: 0:30:28
2025-09-01 16:48:20.784 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 508/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 1.143e-04, size: 416, ETA: 0:30:25
2025-09-01 16:48:23.756 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 508/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 1.140e-04, size: 544, ETA: 0:30:22
2025-09-01 16:48:25.170 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:48:31.546 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:48:32.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:48:32.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6121
2025-09-01 16:48:32.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5256
2025-09-01 16:48:32.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3892
2025-09-01 16:48:32.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5090
2025-09-01 16:48:32.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:48:32.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:48:32.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.612
2025-09-01 16:48:32.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-09-01 16:48:32.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-09-01 16:48:32.600 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.509
2025-09-01 16:48:32.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:48:32.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:48:32.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:48:32.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:48:32.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:48:32.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:48:32.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:48:32.601 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:48:32.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:48:33.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:48:33.564 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:48:34.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:48:34.522 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:48:35.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:48:35.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:48:36.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:48:36.482 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:48:36.969 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:48:36.969 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:48:36.969 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:48:36.969 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:48:36.976 | 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

2025-09-01 16:48:36.977 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:48:37.061 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:48:37.144 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch509
2025-09-01 16:48:40.023 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 509/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, 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: 1.134e-04, size: 384, ETA: 0:30:17
2025-09-01 16:48:43.015 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 509/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 1.130e-04, size: 352, ETA: 0:30:14
2025-09-01 16:48:46.075 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 509/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.127e-04, size: 320, ETA: 0:30:11
2025-09-01 16:48:49.133 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 509/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.9, lr: 1.123e-04, size: 320, ETA: 0:30:08
2025-09-01 16:48:52.246 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 509/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.7, lr: 1.119e-04, size: 416, ETA: 0:30:05
2025-09-01 16:48:55.335 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 509/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 1.116e-04, size: 512, ETA: 0:30:02
2025-09-01 16:48:56.788 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:49:02.968 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:49:03.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:49:04.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5974
2025-09-01 16:49:04.357 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5061
2025-09-01 16:49:04.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3854
2025-09-01 16:49:04.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4963
2025-09-01 16:49:04.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:49:04.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:49:04.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.597
2025-09-01 16:49:04.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-09-01 16:49:04.395 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.385
2025-09-01 16:49:04.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.496
2025-09-01 16:49:04.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:49:04.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:49:04.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:49:04.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:49:04.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:49:04.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:49:04.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:49:04.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:49:04.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:49:05.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:49:05.764 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:49:06.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:49:07.024 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:49:07.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:49:08.341 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:49:08.984 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:49:09.661 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:49:10.296 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:49:10.297 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:49:10.297 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:49:10.297 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:49:10.304 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.90 ms, Average inference time: 7.19 ms

2025-09-01 16:49:10.305 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:49:10.419 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:49:10.493 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch510
2025-09-01 16:49:13.349 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 510/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 11.1, iou_loss: 3.5, l1_loss: 1.4, conf_loss: 5.4, cls_loss: 0.8, lr: 1.110e-04, size: 512, ETA: 0:29:57
2025-09-01 16:49:16.444 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 510/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.9, lr: 1.106e-04, size: 448, ETA: 0:29:54
2025-09-01 16:49:19.608 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 510/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 1.4, cls_loss: 0.7, lr: 1.103e-04, size: 320, ETA: 0:29:51
2025-09-01 16:49:22.627 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 510/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.003s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 1.099e-04, size: 320, ETA: 0:29:48
2025-09-01 16:49:25.708 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 510/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 1.095e-04, size: 576, ETA: 0:29:45
2025-09-01 16:49:28.769 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 510/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, 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: 1.092e-04, size: 416, ETA: 0:29:42
2025-09-01 16:49:30.074 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:49:36.257 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:49:37.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:49:37.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6170
2025-09-01 16:49:37.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5263
2025-09-01 16:49:37.878 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4058
2025-09-01 16:49:37.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5164
2025-09-01 16:49:37.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:49:37.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:49:37.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.617
2025-09-01 16:49:37.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-09-01 16:49:37.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-09-01 16:49:37.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.516
2025-09-01 16:49:37.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:49:37.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:49:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:49:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:49:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:49:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:49:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:49:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:49:37.880 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:49:38.669 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:49:39.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:49:40.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:49:41.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:49:41.745 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:49:42.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:49:43.252 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:49:43.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:49:44.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:49:44.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.28
2025-09-01 16:49:44.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 16:49:44.753 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:49:44.761 | 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

2025-09-01 16:49:44.762 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:49:44.852 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:49:44.936 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch511
2025-09-01 16:49:47.757 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 511/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.086e-04, size: 256, ETA: 0:29:37
2025-09-01 16:49:50.825 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 511/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.7, cls_loss: 0.7, lr: 1.083e-04, size: 384, ETA: 0:29:34
2025-09-01 16:49:53.752 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 511/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 1.079e-04, size: 416, ETA: 0:29:31
2025-09-01 16:49:56.775 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 511/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.7, iou_loss: 3.0, l1_loss: 1.3, conf_loss: 3.7, cls_loss: 0.7, lr: 1.075e-04, size: 288, ETA: 0:29:28
2025-09-01 16:49:59.796 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 511/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, 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: 1.072e-04, size: 352, ETA: 0:29:25
2025-09-01 16:50:02.839 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 511/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.3, iou_loss: 3.2, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.9, lr: 1.068e-04, size: 320, ETA: 0:29:22
2025-09-01 16:50:04.161 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:50:10.564 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:50:11.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:50:12.101 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6086
2025-09-01 16:50:12.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5256
2025-09-01 16:50:12.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4211
2025-09-01 16:50:12.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5184
2025-09-01 16:50:12.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:50:12.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:50:12.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-09-01 16:50:12.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-09-01 16:50:12.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.421
2025-09-01 16:50:12.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.518
2025-09-01 16:50:12.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:50:12.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:50:12.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:50:12.266 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:50:12.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:50:12.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:50:12.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:50:12.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:50:12.268 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:50:13.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:50:13.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:50:14.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:50:15.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:50:16.142 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:50:16.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:50:17.673 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:50:18.452 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:50:19.215 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:50:19.215 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:50:19.215 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 16:50:19.216 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:50:19.223 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.40 ms, Average NMS time: 0.92 ms, Average inference time: 7.33 ms

2025-09-01 16:50:19.223 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:50:19.307 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:50:19.482 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch512
2025-09-01 16:50:22.494 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 512/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 3.1, cls_loss: 0.7, lr: 1.063e-04, size: 352, ETA: 0:29:17
2025-09-01 16:50:25.475 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 512/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 8.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 4.1, cls_loss: 0.7, lr: 1.059e-04, size: 416, ETA: 0:29:14
2025-09-01 16:50:28.404 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 512/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.5, lr: 1.055e-04, size: 288, ETA: 0:29:11
2025-09-01 16:50:31.429 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 512/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 1.052e-04, size: 416, ETA: 0:29:08
2025-09-01 16:50:34.425 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 512/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 1.048e-04, size: 384, ETA: 0:29:05
2025-09-01 16:50:37.389 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 512/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 1.045e-04, size: 416, ETA: 0:29:02
2025-09-01 16:50:38.844 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:50:44.972 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:50:45.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:50:46.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6096
2025-09-01 16:50:46.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5225
2025-09-01 16:50:46.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4115
2025-09-01 16:50:46.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5145
2025-09-01 16:50:46.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:50:46.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:50:46.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.610
2025-09-01 16:50:46.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-09-01 16:50:46.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-09-01 16:50:46.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.515
2025-09-01 16:50:46.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:50:46.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:50:46.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:50:46.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:50:46.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:50:46.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:50:46.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:50:46.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:50:46.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:50:46.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:50:47.355 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:50:47.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:50:48.466 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:50:49.025 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:50:49.631 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:50:50.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:50:50.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:50:51.285 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:50:51.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:50:51.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:50:51.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:50:51.293 | 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: 7.08 ms

2025-09-01 16:50:51.294 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:50:51.382 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:50:51.502 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch513
2025-09-01 16:50:54.449 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 513/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.6, lr: 1.039e-04, size: 544, ETA: 0:28:58
2025-09-01 16:50:57.610 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 513/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.8, lr: 1.036e-04, size: 320, ETA: 0:28:55
2025-09-01 16:51:00.571 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 513/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 21.0, iou_loss: 0.0, l1_loss: 0.0, conf_loss: 21.0, cls_loss: 0.0, lr: 1.032e-04, size: 256, ETA: 0:28:51
2025-09-01 16:51:03.671 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 513/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.6, lr: 1.028e-04, size: 576, ETA: 0:28:48
2025-09-01 16:51:06.685 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 513/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.5, cls_loss: 0.5, lr: 1.025e-04, size: 256, ETA: 0:28:45
2025-09-01 16:51:09.783 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 513/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.7, lr: 1.021e-04, size: 352, ETA: 0:28:42
2025-09-01 16:51:11.152 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:51:17.356 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:51:18.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:51:18.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5981
2025-09-01 16:51:19.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5305
2025-09-01 16:51:19.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3863
2025-09-01 16:51:19.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5050
2025-09-01 16:51:19.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:51:19.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:51:19.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.598
2025-09-01 16:51:19.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-09-01 16:51:19.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.386
2025-09-01 16:51:19.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-09-01 16:51:19.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:51:19.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:51:19.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:51:19.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:51:19.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:51:19.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:51:19.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:51:19.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:51:19.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:51:19.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:51:20.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:51:21.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:51:22.541 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:51:23.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:51:24.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:51:25.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:51:25.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:51:26.755 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:51:26.755 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:51:26.756 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:51:26.756 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:51:26.770 | 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

2025-09-01 16:51:26.770 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:51:26.895 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:51:27.000 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch514
2025-09-01 16:51:29.855 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 514/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 1.016e-04, size: 576, ETA: 0:28:38
2025-09-01 16:51:32.988 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 514/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 1.7, cls_loss: 0.6, lr: 1.013e-04, size: 320, ETA: 0:28:35
2025-09-01 16:51:36.047 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 514/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.8, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 1.009e-04, size: 416, ETA: 0:28:32
2025-09-01 16:51:39.185 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 514/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, 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: 1.005e-04, size: 448, ETA: 0:28:28
2025-09-01 16:51:42.226 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 514/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 1.5, conf_loss: 1.7, cls_loss: 0.7, lr: 1.002e-04, size: 512, ETA: 0:28:25
2025-09-01 16:51:45.408 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 514/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 9.984e-05, size: 576, ETA: 0:28:22
2025-09-01 16:51:46.825 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:51:52.849 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:51:53.529 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:51:53.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6066
2025-09-01 16:51:54.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5186
2025-09-01 16:51:54.083 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3749
2025-09-01 16:51:54.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5000
2025-09-01 16:51:54.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:51:54.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:51:54.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-09-01 16:51:54.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.519
2025-09-01 16:51:54.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.375
2025-09-01 16:51:54.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.500
2025-09-01 16:51:54.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:51:54.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:51:54.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:51:54.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:51:54.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:51:54.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:51:54.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:51:54.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:51:54.085 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:51:54.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:51:55.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:51:55.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:51:56.283 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:51:56.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:51:57.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:51:57.980 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:51:58.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:51:59.094 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:51:59.094 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:51:59.094 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:51:59.094 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:51:59.103 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.20 ms, Average NMS time: 0.86 ms, Average inference time: 7.06 ms

2025-09-01 16:51:59.105 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:51:59.245 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:51:59.318 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch515
2025-09-01 16:52:02.218 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 515/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, 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: 9.933e-05, size: 320, ETA: 0:28:18
2025-09-01 16:52:05.175 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 515/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.4, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.6, lr: 9.898e-05, size: 288, ETA: 0:28:15
2025-09-01 16:52:08.162 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 515/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.6, lr: 9.862e-05, size: 480, ETA: 0:28:12
2025-09-01 16:52:11.174 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 515/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 9.827e-05, size: 384, ETA: 0:28:09
2025-09-01 16:52:14.169 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 515/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.3, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 9.792e-05, size: 320, ETA: 0:28:06
2025-09-01 16:52:17.194 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 515/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 9.757e-05, size: 352, ETA: 0:28:02
2025-09-01 16:52:18.561 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:52:24.839 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:52:25.455 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:52:25.890 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6095
2025-09-01 16:52:25.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5371
2025-09-01 16:52:26.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3607
2025-09-01 16:52:26.001 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5024
2025-09-01 16:52:26.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:52:26.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:52:26.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.610
2025-09-01 16:52:26.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-09-01 16:52:26.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.361
2025-09-01 16:52:26.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 16:52:26.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:52:26.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:52:26.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:52:26.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:52:26.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:52:26.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:52:26.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:52:26.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:52:26.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:52:26.577 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:52:27.098 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:52:27.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:52:28.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:52:28.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:52:29.297 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:52:29.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:52:30.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:52:30.873 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:52:30.874 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:52:30.874 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:52:30.874 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:52:30.880 | 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

2025-09-01 16:52:30.882 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:52:30.964 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:52:31.049 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch516
2025-09-01 16:52:34.085 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 516/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 9.707e-05, size: 384, ETA: 0:27:58
2025-09-01 16:52:37.029 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 516/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 7.4, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.9, lr: 9.672e-05, size: 256, ETA: 0:27:55
2025-09-01 16:52:39.951 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 516/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 7.4, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 9.637e-05, size: 288, ETA: 0:27:52
2025-09-01 16:52:43.001 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 516/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.003s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.8, lr: 9.602e-05, size: 384, ETA: 0:27:49
2025-09-01 16:52:46.047 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 516/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.6, lr: 9.567e-05, size: 576, ETA: 0:27:46
2025-09-01 16:52:49.185 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 516/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, 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: 9.533e-05, size: 384, ETA: 0:27:43
2025-09-01 16:52:50.537 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:52:56.763 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:52:57.479 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:52:58.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6190
2025-09-01 16:52:58.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5369
2025-09-01 16:52:58.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4050
2025-09-01 16:52:58.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5203
2025-09-01 16:52:58.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:52:58.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:52:58.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.619
2025-09-01 16:52:58.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-09-01 16:52:58.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-09-01 16:52:58.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.520
2025-09-01 16:52:58.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:52:58.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:52:58.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:52:58.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:52:58.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:52:58.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:52:58.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:52:58.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:52:58.123 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:52:58.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:52:59.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:52:59.971 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:53:00.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:53:01.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:53:01.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:53:02.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:53:03.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:53:03.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:53:03.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:53:03.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 16:53:03.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:53:03.658 | 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

2025-09-01 16:53:03.659 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:53:03.746 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:53:03.826 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch517
2025-09-01 16:53:06.731 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 517/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, 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: 9.483e-05, size: 288, ETA: 0:27:38
2025-09-01 16:53:09.602 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 517/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 9.448e-05, size: 320, ETA: 0:27:35
2025-09-01 16:53:12.533 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 517/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, 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: 9.414e-05, size: 480, ETA: 0:27:32
2025-09-01 16:53:15.653 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 517/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, 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: 9.380e-05, size: 384, ETA: 0:27:29
2025-09-01 16:53:18.615 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 517/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.5, lr: 9.345e-05, size: 544, ETA: 0:27:26
2025-09-01 16:53:21.679 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 517/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 9.311e-05, size: 288, ETA: 0:27:23
2025-09-01 16:53:23.089 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:53:29.513 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:53:30.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:53:30.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6045
2025-09-01 16:53:31.130 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5198
2025-09-01 16:53:31.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3682
2025-09-01 16:53:31.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4975
2025-09-01 16:53:31.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:53:31.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:53:31.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.605
2025-09-01 16:53:31.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-09-01 16:53:31.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.368
2025-09-01 16:53:31.167 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.497
2025-09-01 16:53:31.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:53:31.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:53:31.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:53:31.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:53:31.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:53:31.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:53:31.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:53:31.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:53:31.168 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:53:32.089 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:53:32.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:53:33.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:53:34.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:53:35.135 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:53:35.938 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:53:36.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:53:37.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:53:38.286 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:53:38.287 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:53:38.287 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:53:38.287 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:53:38.294 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.37 ms, Average NMS time: 0.89 ms, Average inference time: 7.26 ms

2025-09-01 16:53:38.295 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:53:38.381 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:53:38.462 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch518
2025-09-01 16:53:41.342 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 518/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 3.7, cls_loss: 0.7, lr: 9.261e-05, size: 416, ETA: 0:27:18
2025-09-01 16:53:44.401 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 518/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 9.227e-05, size: 256, ETA: 0:27:15
2025-09-01 16:53:47.276 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 518/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.7, lr: 9.193e-05, size: 416, ETA: 0:27:12
2025-09-01 16:53:50.252 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 518/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 9.159e-05, size: 288, ETA: 0:27:09
2025-09-01 16:53:53.301 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 518/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.6, lr: 9.125e-05, size: 576, ETA: 0:27:06
2025-09-01 16:53:56.407 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 518/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.6, lr: 9.092e-05, size: 416, ETA: 0:27:03
2025-09-01 16:53:57.773 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:54:03.935 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:54:04.730 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:54:05.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6130
2025-09-01 16:54:05.370 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5378
2025-09-01 16:54:05.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4259
2025-09-01 16:54:05.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5256
2025-09-01 16:54:05.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:54:05.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:54:05.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.613
2025-09-01 16:54:05.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.538
2025-09-01 16:54:05.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-09-01 16:54:05.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.526
2025-09-01 16:54:05.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:54:05.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:54:05.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:54:05.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:54:05.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:54:05.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:54:05.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:54:05.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:54:05.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:54:06.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:54:06.768 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:54:07.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:54:08.125 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:54:08.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:54:09.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:54:10.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:54:10.801 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:54:11.489 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:54:11.489 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:54:11.489 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.53
2025-09-01 16:54:11.490 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:54:11.497 | 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

2025-09-01 16:54:11.498 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:54:11.591 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:54:11.670 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch519
2025-09-01 16:54:14.595 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 519/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.5, lr: 9.043e-05, size: 576, ETA: 0:26:58
2025-09-01 16:54:17.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 519/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 9.009e-05, size: 544, ETA: 0:26:55
2025-09-01 16:54:20.801 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 519/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, 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: 8.975e-05, size: 480, ETA: 0:26:52
2025-09-01 16:54:23.856 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 519/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 8.942e-05, size: 288, ETA: 0:26:49
2025-09-01 16:54:26.781 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 519/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 8.908e-05, size: 480, ETA: 0:26:46
2025-09-01 16:54:29.879 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 519/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 8.875e-05, size: 480, ETA: 0:26:43
2025-09-01 16:54:31.268 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:54:37.497 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:54:38.249 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:54:38.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6057
2025-09-01 16:54:38.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5173
2025-09-01 16:54:38.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4097
2025-09-01 16:54:38.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5109
2025-09-01 16:54:38.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:54:38.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:54:38.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.606
2025-09-01 16:54:38.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 16:54:38.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-09-01 16:54:38.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-09-01 16:54:38.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:54:38.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:54:38.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:54:38.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:54:38.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:54:38.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:54:38.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:54:38.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:54:38.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:54:39.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:54:40.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:54:40.834 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:54:41.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:54:42.093 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:54:42.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:54:43.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:54:43.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:54:44.577 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:54:44.577 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:54:44.577 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:54:44.577 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:54:44.585 | 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

2025-09-01 16:54:44.587 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:54:44.680 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:54:44.800 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch520
2025-09-01 16:54:47.673 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 520/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 8.5, iou_loss: 3.3, l1_loss: 1.1, conf_loss: 3.3, cls_loss: 0.8, lr: 8.826e-05, size: 256, ETA: 0:26:38
2025-09-01 16:54:50.713 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 520/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 8.793e-05, size: 384, ETA: 0:26:35
2025-09-01 16:54:53.702 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 520/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 8.760e-05, size: 448, ETA: 0:26:32
2025-09-01 16:54:56.786 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 520/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.9, iou_loss: 1.4, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.5, lr: 8.727e-05, size: 448, ETA: 0:26:29
2025-09-01 16:54:59.885 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 520/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.7, lr: 8.693e-05, size: 288, ETA: 0:26:26
2025-09-01 16:55:02.877 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 520/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, 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: 8.660e-05, size: 384, ETA: 0:26:23
2025-09-01 16:55:04.279 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:55:10.501 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:55:11.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:55:11.913 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6134
2025-09-01 16:55:12.040 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5233
2025-09-01 16:55:12.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4093
2025-09-01 16:55:12.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5153
2025-09-01 16:55:12.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:55:12.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:55:12.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.613
2025-09-01 16:55:12.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-09-01 16:55:12.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-09-01 16:55:12.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.515
2025-09-01 16:55:12.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:55:12.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:55:12.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:55:12.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:55:12.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:55:12.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:55:12.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:55:12.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:55:12.122 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:55:12.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:55:13.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:55:14.264 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:55:14.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:55:15.681 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:55:16.381 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:55:17.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:55:17.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:55:18.485 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:55:18.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:55:18.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 16:55:18.486 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:55:18.493 | 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: 7.07 ms

2025-09-01 16:55:18.495 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:55:18.616 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:55:18.695 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch521
2025-09-01 16:55:21.663 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 521/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 1.0, lr: 8.612e-05, size: 512, ETA: 0:26:19
2025-09-01 16:55:24.690 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 521/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.9, lr: 8.580e-05, size: 480, ETA: 0:26:15
2025-09-01 16:55:27.780 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 521/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, 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: 8.547e-05, size: 320, ETA: 0:26:12
2025-09-01 16:55:30.838 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 521/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 1.1, conf_loss: 1.4, cls_loss: 0.6, lr: 8.514e-05, size: 544, ETA: 0:26:09
2025-09-01 16:55:33.967 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 521/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 8.481e-05, size: 448, ETA: 0:26:06
2025-09-01 16:55:36.934 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 521/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 8.448e-05, size: 416, ETA: 0:26:03
2025-09-01 16:55:38.215 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:55:44.451 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:55:45.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:55:46.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6098
2025-09-01 16:55:46.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5315
2025-09-01 16:55:46.194 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4260
2025-09-01 16:55:46.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5224
2025-09-01 16:55:46.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:55:46.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:55:46.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.610
2025-09-01 16:55:46.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-09-01 16:55:46.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.426
2025-09-01 16:55:46.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.522
2025-09-01 16:55:46.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:55:46.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:55:46.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:55:46.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:55:46.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:55:46.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:55:46.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:55:46.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:55:46.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:55:47.038 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:55:47.833 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:55:48.665 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:55:49.444 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:55:50.260 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:55:51.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:55:51.862 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:55:52.651 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:55:53.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:55:53.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:55:53.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 16:55:53.479 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:55:53.487 | 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

2025-09-01 16:55:53.488 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:55:53.570 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:55:53.652 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch522
2025-09-01 16:55:56.407 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 522/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.136s, 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: 8.401e-05, size: 288, ETA: 0:25:59
2025-09-01 16:55:59.410 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 522/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 3.9, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.6, lr: 8.369e-05, size: 352, ETA: 0:25:56
2025-09-01 16:56:02.368 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 522/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 8.3, iou_loss: 2.9, l1_loss: 1.2, conf_loss: 3.5, cls_loss: 0.7, lr: 8.336e-05, size: 544, ETA: 0:25:53
2025-09-01 16:56:05.340 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 522/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, 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: 8.304e-05, size: 448, ETA: 0:25:49
2025-09-01 16:56:08.403 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 522/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 1.7, cls_loss: 0.6, lr: 8.271e-05, size: 576, ETA: 0:25:46
2025-09-01 16:56:11.728 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 522/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.164s, data_time: 0.002s, total_loss: 2.5, iou_loss: 1.2, l1_loss: 0.3, conf_loss: 0.7, cls_loss: 0.4, lr: 8.239e-05, size: 544, ETA: 0:25:43
2025-09-01 16:56:13.128 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:56:19.305 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:56:19.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:56:20.419 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6075
2025-09-01 16:56:20.562 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5302
2025-09-01 16:56:20.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3768
2025-09-01 16:56:20.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5048
2025-09-01 16:56:20.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:56:20.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:56:20.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-09-01 16:56:20.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.530
2025-09-01 16:56:20.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-09-01 16:56:20.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-09-01 16:56:20.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:56:20.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:56:20.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:56:20.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:56:20.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:56:20.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:56:20.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:56:20.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:56:20.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:56:21.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:56:21.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:56:22.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:56:22.967 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:56:23.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:56:24.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:56:24.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:56:25.299 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:56:25.882 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:56:25.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:56:25.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 16:56:25.883 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:56:25.890 | 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

2025-09-01 16:56:25.891 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:56:25.981 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:56:26.066 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch523
2025-09-01 16:56:28.997 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 523/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.4, cls_loss: 0.6, lr: 8.192e-05, size: 320, ETA: 0:25:39
2025-09-01 16:56:32.048 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 523/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 8.160e-05, size: 288, ETA: 0:25:36
2025-09-01 16:56:35.108 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 523/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, 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: 8.128e-05, size: 512, ETA: 0:25:33
2025-09-01 16:56:38.172 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 523/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.006s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.5, lr: 8.096e-05, size: 448, ETA: 0:25:30
2025-09-01 16:56:41.211 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 523/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, 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: 8.064e-05, size: 384, ETA: 0:25:27
2025-09-01 16:56:44.196 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 523/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.2, cls_loss: 0.7, lr: 8.032e-05, size: 320, ETA: 0:25:23
2025-09-01 16:56:45.509 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:56:51.637 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:56:52.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:56:52.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5902
2025-09-01 16:56:52.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5013
2025-09-01 16:56:52.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.2875
2025-09-01 16:56:52.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4597
2025-09-01 16:56:52.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:56:52.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:56:52.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.590
2025-09-01 16:56:52.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.501
2025-09-01 16:56:52.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.287
2025-09-01 16:56:52.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.460
2025-09-01 16:56:52.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:56:52.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:56:52.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:56:52.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:56:52.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:56:52.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:56:52.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:56:52.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:56:52.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:56:52.803 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:56:53.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:56:53.510 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:56:53.847 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:56:54.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:56:54.534 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:56:54.874 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:56:55.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:56:55.626 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:56:55.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 16:56:55.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.46
2025-09-01 16:56:55.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:56:55.633 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.24 ms, Average NMS time: 0.77 ms, Average inference time: 7.01 ms

2025-09-01 16:56:55.633 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:56:55.751 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:56:55.823 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch524
2025-09-01 16:56:58.688 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 524/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.7, cls_loss: 0.7, lr: 7.986e-05, size: 320, ETA: 0:25:19
2025-09-01 16:57:01.740 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 524/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 7.954e-05, size: 480, ETA: 0:25:16
2025-09-01 16:57:04.778 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 524/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 7.923e-05, size: 320, ETA: 0:25:13
2025-09-01 16:57:07.853 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 524/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.9, conf_loss: 0.9, cls_loss: 0.5, lr: 7.891e-05, size: 544, ETA: 0:25:10
2025-09-01 16:57:10.890 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 524/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.8, lr: 7.859e-05, size: 352, ETA: 0:25:07
2025-09-01 16:57:13.913 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 524/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, 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: 7.828e-05, size: 352, ETA: 0:25:04
2025-09-01 16:57:15.254 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:57:21.372 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:57:22.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:57:22.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6079
2025-09-01 16:57:22.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5265
2025-09-01 16:57:22.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4056
2025-09-01 16:57:22.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5133
2025-09-01 16:57:22.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:57:22.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:57:22.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-09-01 16:57:22.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.527
2025-09-01 16:57:22.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-09-01 16:57:22.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-09-01 16:57:22.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:57:22.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:57:22.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:57:22.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:57:22.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:57:22.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:57:22.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:57:22.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:57:22.729 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:57:23.445 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:57:24.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:57:24.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:57:25.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:57:25.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:57:26.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:57:26.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:57:27.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:57:28.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:57:28.030 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:57:28.030 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:57:28.030 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:57:28.037 | 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

2025-09-01 16:57:28.038 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:57:28.119 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:57:28.210 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch525
2025-09-01 16:57:31.109 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 525/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.6, conf_loss: 2.3, cls_loss: 0.6, lr: 7.782e-05, size: 352, ETA: 0:24:59
2025-09-01 16:57:34.124 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 525/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.7, lr: 7.751e-05, size: 320, ETA: 0:24:56
2025-09-01 16:57:37.205 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 525/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.7, lr: 7.720e-05, size: 576, ETA: 0:24:53
2025-09-01 16:57:40.326 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 525/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 6.6, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.6, lr: 7.688e-05, size: 384, ETA: 0:24:50
2025-09-01 16:57:43.250 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 525/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 7.657e-05, size: 320, ETA: 0:24:47
2025-09-01 16:57:46.227 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 525/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 0.8, cls_loss: 0.5, lr: 7.626e-05, size: 512, ETA: 0:24:44
2025-09-01 16:57:47.571 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:57:53.971 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:57:54.807 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:57:55.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6151
2025-09-01 16:57:55.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5112
2025-09-01 16:57:55.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3954
2025-09-01 16:57:55.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5072
2025-09-01 16:57:55.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:57:55.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:57:55.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.615
2025-09-01 16:57:55.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-09-01 16:57:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.395
2025-09-01 16:57:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.507
2025-09-01 16:57:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:57:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:57:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:57:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:57:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:57:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:57:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:57:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:57:55.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:57:56.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:57:56.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:57:57.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:57:57.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:57:58.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:57:59.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:57:59.891 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:58:00.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:58:01.156 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:58:01.156 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 16:58:01.156 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:58:01.156 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:58:01.163 | 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

2025-09-01 16:58:01.164 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:58:01.246 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:58:01.328 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch526
2025-09-01 16:58:04.427 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 526/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, 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: 7.581e-05, size: 512, ETA: 0:24:39
2025-09-01 16:58:07.511 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 526/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.8, lr: 7.550e-05, size: 256, ETA: 0:24:36
2025-09-01 16:58:10.581 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 526/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 1.0, lr: 7.519e-05, size: 288, ETA: 0:24:33
2025-09-01 16:58:13.621 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 526/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 7.488e-05, size: 384, ETA: 0:24:30
2025-09-01 16:58:16.646 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 526/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.7, cls_loss: 0.6, lr: 7.457e-05, size: 576, ETA: 0:24:27
2025-09-01 16:58:19.710 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 526/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 7.427e-05, size: 544, ETA: 0:24:24
2025-09-01 16:58:21.112 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:58:27.244 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:58:27.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:58:28.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6095
2025-09-01 16:58:28.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5166
2025-09-01 16:58:28.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4096
2025-09-01 16:58:28.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5119
2025-09-01 16:58:28.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:58:28.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:58:28.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-09-01 16:58:28.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 16:58:28.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-09-01 16:58:28.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.512
2025-09-01 16:58:28.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:58:28.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:58:28.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:58:28.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:58:28.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:58:28.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:58:28.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:58:28.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:58:28.521 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:58:29.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:58:29.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:58:30.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:58:30.858 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:58:31.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:58:31.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:58:32.486 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:58:33.029 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:58:33.614 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:58:33.614 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:58:33.614 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:58:33.614 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:58:33.621 | 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

2025-09-01 16:58:33.622 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:58:33.708 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:58:33.790 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch527
2025-09-01 16:58:36.870 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 527/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.5, lr: 7.382e-05, size: 320, ETA: 0:24:19
2025-09-01 16:58:39.901 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 527/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, 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: 7.352e-05, size: 352, ETA: 0:24:16
2025-09-01 16:58:43.122 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 527/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 7.321e-05, size: 352, ETA: 0:24:13
2025-09-01 16:58:46.229 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 527/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, 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: 7.291e-05, size: 416, ETA: 0:24:10
2025-09-01 16:58:49.157 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 527/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 7.260e-05, size: 384, ETA: 0:24:07
2025-09-01 16:58:52.132 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 527/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, 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: 7.230e-05, size: 480, ETA: 0:24:04
2025-09-01 16:58:53.487 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:58:59.557 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:59:00.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:59:01.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6110
2025-09-01 16:59:01.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5158
2025-09-01 16:59:01.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4175
2025-09-01 16:59:01.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5148
2025-09-01 16:59:01.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:59:01.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:59:01.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.611
2025-09-01 16:59:01.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-09-01 16:59:01.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.418
2025-09-01 16:59:01.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.515
2025-09-01 16:59:01.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:59:01.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:59:01.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:59:01.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:59:01.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:59:01.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:59:01.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:59:01.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:59:01.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:59:02.183 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:59:03.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:59:03.787 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:59:04.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:59:05.413 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:59:06.240 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:59:07.045 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:59:07.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:59:08.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:59:08.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.28
2025-09-01 16:59:08.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 16:59:08.651 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:59:08.659 | 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

2025-09-01 16:59:08.664 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:59:08.753 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:59:08.874 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch528
2025-09-01 16:59:11.765 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 528/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 8.6, iou_loss: 3.3, l1_loss: 1.0, conf_loss: 3.6, cls_loss: 0.7, lr: 7.186e-05, size: 416, ETA: 0:24:00
2025-09-01 16:59:14.802 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 528/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.6, lr: 7.156e-05, size: 256, ETA: 0:23:57
2025-09-01 16:59:17.731 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 528/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.5, lr: 7.126e-05, size: 320, ETA: 0:23:53
2025-09-01 16:59:20.795 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 528/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 7.096e-05, size: 416, ETA: 0:23:50
2025-09-01 16:59:24.003 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 528/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.158s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.9, lr: 7.066e-05, size: 416, ETA: 0:23:47
2025-09-01 16:59:27.176 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 528/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, 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: 7.036e-05, size: 544, ETA: 0:23:44
2025-09-01 16:59:28.630 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:59:34.843 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 16:59:35.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 16:59:36.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6165
2025-09-01 16:59:36.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5373
2025-09-01 16:59:36.208 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4152
2025-09-01 16:59:36.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5230
2025-09-01 16:59:36.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 16:59:36.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 16:59:36.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.617
2025-09-01 16:59:36.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-09-01 16:59:36.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.415
2025-09-01 16:59:36.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.523
2025-09-01 16:59:36.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 16:59:36.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 16:59:36.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 16:59:36.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 16:59:36.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 16:59:36.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 16:59:36.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 16:59:36.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 16:59:36.211 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 16:59:36.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 16:59:37.446 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 16:59:38.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 16:59:38.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 16:59:39.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 16:59:39.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 16:59:40.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 16:59:41.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 16:59:41.682 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 16:59:41.682 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 16:59:41.682 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 16:59:41.682 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 16:59:41.689 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.84 ms, Average inference time: 7.12 ms

2025-09-01 16:59:41.690 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:59:41.777 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 16:59:41.858 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch529
2025-09-01 16:59:44.722 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 529/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 6.992e-05, size: 256, ETA: 0:23:40
2025-09-01 16:59:47.867 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 529/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 6.963e-05, size: 256, ETA: 0:23:37
2025-09-01 16:59:50.741 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 529/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 8.2, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.7, lr: 6.933e-05, size: 320, ETA: 0:23:34
2025-09-01 16:59:53.749 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 529/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, 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: 6.903e-05, size: 448, ETA: 0:23:31
2025-09-01 16:59:56.866 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 529/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.7, lr: 6.874e-05, size: 480, ETA: 0:23:28
2025-09-01 16:59:59.972 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 529/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 6.844e-05, size: 448, ETA: 0:23:24
2025-09-01 17:00:01.367 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:00:07.567 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:00:08.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:00:08.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5890
2025-09-01 17:00:08.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4946
2025-09-01 17:00:08.586 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3705
2025-09-01 17:00:08.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4847
2025-09-01 17:00:08.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:00:08.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:00:08.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.589
2025-09-01 17:00:08.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.495
2025-09-01 17:00:08.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.370
2025-09-01 17:00:08.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.485
2025-09-01 17:00:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:00:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:00:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:00:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:00:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:00:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:00:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:00:08.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:00:08.589 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:00:09.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:00:09.461 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:00:09.886 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:00:10.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:00:10.725 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:00:11.153 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:00:11.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:00:12.006 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:00:12.439 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:00:12.439 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 17:00:12.439 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.48
2025-09-01 17:00:12.439 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:00:12.445 | 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

2025-09-01 17:00:12.446 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:00:12.536 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:00:12.618 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch530
2025-09-01 17:00:15.465 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 530/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.3, cls_loss: 0.6, lr: 6.801e-05, size: 352, ETA: 0:23:20
2025-09-01 17:00:18.400 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 530/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.6, lr: 6.772e-05, size: 544, ETA: 0:23:17
2025-09-01 17:00:21.447 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 530/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 3.4, cls_loss: 0.8, lr: 6.743e-05, size: 320, ETA: 0:23:14
2025-09-01 17:00:24.540 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 530/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.003s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.4, conf_loss: 1.3, cls_loss: 0.6, lr: 6.713e-05, size: 288, ETA: 0:23:11
2025-09-01 17:00:27.550 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 530/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 6.684e-05, size: 544, ETA: 0:23:08
2025-09-01 17:00:30.590 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 530/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 6.655e-05, size: 512, ETA: 0:23:05
2025-09-01 17:00:31.982 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:00:38.136 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:00:38.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:00:39.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6150
2025-09-01 17:00:39.539 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5325
2025-09-01 17:00:39.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3943
2025-09-01 17:00:39.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5139
2025-09-01 17:00:39.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:00:39.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:00:39.573 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.615
2025-09-01 17:00:39.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-09-01 17:00:39.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-09-01 17:00:39.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-09-01 17:00:39.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:00:39.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:00:39.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:00:39.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:00:39.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:00:39.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:00:39.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:00:39.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:00:39.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:00:40.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:00:40.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:00:41.563 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:00:42.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:00:42.894 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:00:43.540 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:00:44.209 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:00:44.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:00:45.542 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:00:45.542 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:00:45.542 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:00:45.542 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:00:45.550 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.32 ms, Average NMS time: 0.89 ms, Average inference time: 7.21 ms

2025-09-01 17:00:45.550 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:00:45.638 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:00:45.721 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch531
2025-09-01 17:00:48.540 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 531/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 6.613e-05, size: 288, ETA: 0:23:00
2025-09-01 17:00:51.796 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 531/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.162s, 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: 6.584e-05, size: 352, ETA: 0:22:57
2025-09-01 17:00:54.792 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 531/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, 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: 6.555e-05, size: 544, ETA: 0:22:54
2025-09-01 17:00:57.998 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 531/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.158s, data_time: 0.005s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 1.2, cls_loss: 0.6, lr: 6.526e-05, size: 512, ETA: 0:22:51
2025-09-01 17:01:01.160 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 531/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.0, cls_loss: 0.6, lr: 6.497e-05, size: 576, ETA: 0:22:48
2025-09-01 17:01:04.274 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 531/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 6.469e-05, size: 480, ETA: 0:22:45
2025-09-01 17:01:05.642 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:01:11.957 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:01:12.713 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:01:13.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6094
2025-09-01 17:01:13.317 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5252
2025-09-01 17:01:13.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4134
2025-09-01 17:01:13.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5160
2025-09-01 17:01:13.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:01:13.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:01:13.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-09-01 17:01:13.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-09-01 17:01:13.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-09-01 17:01:13.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.516
2025-09-01 17:01:13.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:01:13.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:01:13.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:01:13.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:01:13.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:01:13.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:01:13.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:01:13.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:01:13.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:01:14.092 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:01:14.723 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:01:15.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:01:15.974 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:01:16.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:01:17.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:01:17.830 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:01:18.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:01:19.047 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:01:19.047 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:01:19.047 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:01:19.047 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:01:19.054 | 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: 7.01 ms

2025-09-01 17:01:19.055 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:01:19.143 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:01:19.227 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch532
2025-09-01 17:01:22.140 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 532/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 6.427e-05, size: 416, ETA: 0:22:40
2025-09-01 17:01:25.293 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 532/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.6, lr: 6.398e-05, size: 352, ETA: 0:22:37
2025-09-01 17:01:28.485 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 532/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 1.4, lr: 6.370e-05, size: 288, ETA: 0:22:34
2025-09-01 17:01:31.612 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 532/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.005s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.7, lr: 6.341e-05, size: 448, ETA: 0:22:31
2025-09-01 17:01:34.648 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 532/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, 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: 6.313e-05, size: 352, ETA: 0:22:28
2025-09-01 17:01:37.641 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 532/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 6.285e-05, size: 448, ETA: 0:22:25
2025-09-01 17:01:38.980 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:01:45.370 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:01:46.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:01:47.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6158
2025-09-01 17:01:47.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5202
2025-09-01 17:01:47.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4062
2025-09-01 17:01:47.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5141
2025-09-01 17:01:47.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:01:47.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:01:47.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.616
2025-09-01 17:01:47.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.520
2025-09-01 17:01:47.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-09-01 17:01:47.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-09-01 17:01:47.262 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:01:47.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:01:47.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:01:47.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:01:47.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:01:47.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:01:47.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:01:47.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:01:47.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:01:48.129 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:01:49.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:01:49.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:01:50.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:01:51.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:01:52.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:01:53.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:01:54.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:01:55.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:01:55.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:01:55.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:01:55.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:01:55.181 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.37 ms, Average NMS time: 0.91 ms, Average inference time: 7.29 ms

2025-09-01 17:01:55.182 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:01:55.270 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:01:55.351 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch533
2025-09-01 17:01:58.275 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 533/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.7, lr: 6.244e-05, size: 480, ETA: 0:22:21
2025-09-01 17:02:01.255 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 533/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, 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: 6.215e-05, size: 288, ETA: 0:22:17
2025-09-01 17:02:04.278 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 533/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, 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: 6.187e-05, size: 288, ETA: 0:22:14
2025-09-01 17:02:07.354 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 533/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.7, lr: 6.159e-05, size: 416, ETA: 0:22:11
2025-09-01 17:02:10.423 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 533/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.8, lr: 6.131e-05, size: 320, ETA: 0:22:08
2025-09-01 17:02:13.412 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 533/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 6.103e-05, size: 384, ETA: 0:22:05
2025-09-01 17:02:14.766 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:02:21.172 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:02:21.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:02:22.191 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6054
2025-09-01 17:02:22.327 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5173
2025-09-01 17:02:22.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3713
2025-09-01 17:02:22.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4980
2025-09-01 17:02:22.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:02:22.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:02:22.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.605
2025-09-01 17:02:22.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 17:02:22.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.371
2025-09-01 17:02:22.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.498
2025-09-01 17:02:22.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:02:22.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:02:22.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:02:22.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:02:22.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:02:22.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:02:22.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:02:22.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:02:22.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:02:23.030 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:02:23.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:02:24.082 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:02:24.652 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:02:25.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:02:25.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:02:26.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:02:26.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:02:27.291 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:02:27.291 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:02:27.291 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 17:02:27.292 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:02:27.298 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.07 ms, Average NMS time: 0.87 ms, Average inference time: 6.94 ms

2025-09-01 17:02:27.300 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:02:27.383 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:02:27.464 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch534
2025-09-01 17:02:30.378 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 534/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.8, lr: 6.063e-05, size: 384, ETA: 0:22:01
2025-09-01 17:02:33.378 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 534/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.2, iou_loss: 1.9, l1_loss: 1.1, conf_loss: 1.6, cls_loss: 0.6, lr: 6.035e-05, size: 512, ETA: 0:21:58
2025-09-01 17:02:36.435 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 534/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 6.007e-05, size: 384, ETA: 0:21:55
2025-09-01 17:02:39.531 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 534/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 5.979e-05, size: 256, ETA: 0:21:52
2025-09-01 17:02:42.452 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 534/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 1.1, cls_loss: 0.5, lr: 5.952e-05, size: 448, ETA: 0:21:48
2025-09-01 17:02:45.428 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 534/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.7, lr: 5.924e-05, size: 256, ETA: 0:21:45
2025-09-01 17:02:46.820 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:02:52.985 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:02:53.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:02:54.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6169
2025-09-01 17:02:54.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5128
2025-09-01 17:02:54.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4102
2025-09-01 17:02:54.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5133
2025-09-01 17:02:54.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:02:54.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:02:54.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.617
2025-09-01 17:02:54.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-09-01 17:02:54.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-09-01 17:02:54.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-09-01 17:02:54.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:02:54.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:02:54.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:02:54.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:02:54.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:02:54.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:02:54.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:02:54.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:02:54.475 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:02:55.136 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:02:55.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:02:56.502 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:02:57.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:02:57.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:02:58.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:02:59.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:02:59.855 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:03:00.550 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:03:00.551 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:03:00.551 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:03:00.551 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:03:00.558 | 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

2025-09-01 17:03:00.559 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:03:00.649 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:03:00.732 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch535
2025-09-01 17:03:03.578 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 535/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 5.884e-05, size: 384, ETA: 0:21:41
2025-09-01 17:03:06.560 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 535/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.7, lr: 5.857e-05, size: 288, ETA: 0:21:38
2025-09-01 17:03:09.495 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 535/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.5, lr: 5.830e-05, size: 384, ETA: 0:21:35
2025-09-01 17:03:12.610 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 535/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, 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: 5.802e-05, size: 352, ETA: 0:21:32
2025-09-01 17:03:15.755 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 535/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, 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: 5.775e-05, size: 544, ETA: 0:21:29
2025-09-01 17:03:18.754 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 535/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, 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: 5.748e-05, size: 288, ETA: 0:21:25
2025-09-01 17:03:20.094 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:03:26.514 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:03:27.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:03:27.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6062
2025-09-01 17:03:27.951 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5158
2025-09-01 17:03:27.988 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3970
2025-09-01 17:03:27.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5064
2025-09-01 17:03:27.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:03:27.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:03:27.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.606
2025-09-01 17:03:27.989 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-09-01 17:03:27.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-09-01 17:03:27.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.506
2025-09-01 17:03:27.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:03:27.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:03:27.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:03:27.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:03:27.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:03:27.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:03:27.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:03:27.990 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:03:27.991 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:03:28.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:03:29.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:03:30.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:03:30.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:03:31.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:03:31.978 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:03:32.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:03:33.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:03:33.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:03:33.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:03:33.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:03:33.941 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:03:33.948 | 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

2025-09-01 17:03:33.949 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:03:34.039 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:03:34.129 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch536
2025-09-01 17:03:37.114 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 536/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.9, cls_loss: 0.9, lr: 5.709e-05, size: 384, ETA: 0:21:21
2025-09-01 17:03:40.094 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 536/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 5.682e-05, size: 480, ETA: 0:21:18
2025-09-01 17:03:43.269 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 536/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.157s, 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: 5.655e-05, size: 320, ETA: 0:21:15
2025-09-01 17:03:46.334 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 536/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.5, lr: 5.628e-05, size: 416, ETA: 0:21:12
2025-09-01 17:03:49.302 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 536/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 5.601e-05, size: 480, ETA: 0:21:09
2025-09-01 17:03:52.276 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 536/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 1.0, lr: 5.574e-05, size: 352, ETA: 0:21:06
2025-09-01 17:03:53.652 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:03:59.657 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:04:00.664 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:04:01.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6155
2025-09-01 17:04:01.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5262
2025-09-01 17:04:01.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4087
2025-09-01 17:04:01.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5168
2025-09-01 17:04:01.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:04:01.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:04:01.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.615
2025-09-01 17:04:01.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-09-01 17:04:01.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-09-01 17:04:01.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.517
2025-09-01 17:04:01.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:04:01.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:04:01.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:04:01.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:04:01.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:04:01.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:04:01.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:04:01.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:04:01.599 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:04:02.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:04:03.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:04:04.415 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:04:05.312 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:04:06.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:04:07.079 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:04:08.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:04:08.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:04:09.758 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:04:09.758 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:04:09.758 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:04:09.759 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:04:09.771 | 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

2025-09-01 17:04:09.772 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:04:09.890 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:04:09.992 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch537
2025-09-01 17:04:12.928 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 537/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 0.6, conf_loss: 2.9, cls_loss: 0.6, lr: 5.536e-05, size: 544, ETA: 0:21:01
2025-09-01 17:04:16.159 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 537/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.8, cls_loss: 0.6, lr: 5.509e-05, size: 320, ETA: 0:20:58
2025-09-01 17:04:19.214 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 537/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, 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: 5.483e-05, size: 576, ETA: 0:20:55
2025-09-01 17:04:22.325 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 537/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 3.0, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 5.456e-05, size: 352, ETA: 0:20:52
2025-09-01 17:04:25.272 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 537/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, 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: 5.430e-05, size: 384, ETA: 0:20:49
2025-09-01 17:04:28.208 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 537/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.6, lr: 5.403e-05, size: 480, ETA: 0:20:46
2025-09-01 17:04:29.646 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:04:35.954 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:04:36.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:04:37.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6126
2025-09-01 17:04:37.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5338
2025-09-01 17:04:37.554 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3876
2025-09-01 17:04:37.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5113
2025-09-01 17:04:37.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:04:37.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:04:37.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.613
2025-09-01 17:04:37.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.534
2025-09-01 17:04:37.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-09-01 17:04:37.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-09-01 17:04:37.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:04:37.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:04:37.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:04:37.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:04:37.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:04:37.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:04:37.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:04:37.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:04:37.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:04:38.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:04:39.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:04:39.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:04:40.543 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:04:41.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:04:41.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:04:42.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:04:43.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:04:44.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:04:44.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:04:44.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:04:44.165 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:04:44.173 | 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: 7.04 ms

2025-09-01 17:04:44.173 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:04:44.256 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:04:44.339 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch538
2025-09-01 17:04:47.181 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 538/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.140s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.4, cls_loss: 0.6, lr: 5.365e-05, size: 512, ETA: 0:20:41
2025-09-01 17:04:50.293 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 538/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.6, lr: 5.339e-05, size: 576, ETA: 0:20:38
2025-09-01 17:04:53.373 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 538/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 5.313e-05, size: 544, ETA: 0:20:35
2025-09-01 17:04:56.394 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 538/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, 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: 5.287e-05, size: 352, ETA: 0:20:32
2025-09-01 17:04:59.442 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 538/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 2.9, iou_loss: 1.3, l1_loss: 0.3, conf_loss: 0.8, cls_loss: 0.4, lr: 5.261e-05, size: 320, ETA: 0:20:29
2025-09-01 17:05:02.629 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 538/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.5, lr: 5.235e-05, size: 480, ETA: 0:20:26
2025-09-01 17:05:04.030 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:05:10.141 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:05:10.821 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:05:11.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6006
2025-09-01 17:05:11.270 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5254
2025-09-01 17:05:11.342 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3940
2025-09-01 17:05:11.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5067
2025-09-01 17:05:11.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:05:11.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:05:11.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.601
2025-09-01 17:05:11.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-09-01 17:05:11.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-09-01 17:05:11.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.507
2025-09-01 17:05:11.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:05:11.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:05:11.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:05:11.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:05:11.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:05:11.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:05:11.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:05:11.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:05:11.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:05:11.877 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:05:12.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:05:12.893 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:05:13.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:05:13.940 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:05:14.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:05:15.046 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:05:15.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:05:16.076 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:05:16.076 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:05:16.076 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:05:16.076 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:05:16.083 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.35 ms, Average NMS time: 0.88 ms, Average inference time: 7.23 ms

2025-09-01 17:05:16.084 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:05:16.172 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:05:16.253 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch539
2025-09-01 17:05:19.178 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 539/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.6, iou_loss: 1.9, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.5, lr: 5.197e-05, size: 448, ETA: 0:20:22
2025-09-01 17:05:22.106 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 539/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, 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: 5.171e-05, size: 384, ETA: 0:20:19
2025-09-01 17:05:25.172 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 539/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.1, cls_loss: 0.6, lr: 5.146e-05, size: 448, ETA: 0:20:15
2025-09-01 17:05:28.298 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 539/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.003s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 5.120e-05, size: 320, ETA: 0:20:12
2025-09-01 17:05:31.187 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 539/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.6, lr: 5.094e-05, size: 416, ETA: 0:20:09
2025-09-01 17:05:34.249 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 539/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 5.069e-05, size: 416, ETA: 0:20:06
2025-09-01 17:05:35.597 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:05:41.693 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:05:42.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:05:42.926 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6000
2025-09-01 17:05:43.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5493
2025-09-01 17:05:43.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3974
2025-09-01 17:05:43.105 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5156
2025-09-01 17:05:43.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:05:43.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:05:43.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.600
2025-09-01 17:05:43.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.549
2025-09-01 17:05:43.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-09-01 17:05:43.106 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.516
2025-09-01 17:05:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:05:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:05:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:05:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:05:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:05:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:05:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:05:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:05:43.107 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:05:43.767 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:05:44.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:05:44.999 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:05:45.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:05:46.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:05:46.933 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:05:47.583 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:05:48.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:05:48.824 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:05:48.824 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:05:48.824 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:05:48.824 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:05:48.834 | 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

2025-09-01 17:05:48.834 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:05:48.924 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:05:49.005 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch540
2025-09-01 17:05:51.968 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 540/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 5.032e-05, size: 256, ETA: 0:20:02
2025-09-01 17:05:54.976 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 540/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.5, lr: 5.007e-05, size: 480, ETA: 0:19:59
2025-09-01 17:05:58.001 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 540/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, 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: 4.981e-05, size: 512, ETA: 0:19:56
2025-09-01 17:06:01.070 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 540/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.9, lr: 4.956e-05, size: 480, ETA: 0:19:53
2025-09-01 17:06:04.177 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 540/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.6, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.5, lr: 4.931e-05, size: 384, ETA: 0:19:49
2025-09-01 17:06:07.175 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 540/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.5, lr: 4.906e-05, size: 320, ETA: 0:19:46
2025-09-01 17:06:08.517 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:06:15.076 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:06:16.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:06:16.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6033
2025-09-01 17:06:16.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.4905
2025-09-01 17:06:16.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3928
2025-09-01 17:06:16.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4955
2025-09-01 17:06:16.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:06:16.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:06:16.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.603
2025-09-01 17:06:16.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.491
2025-09-01 17:06:16.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-09-01 17:06:16.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.496
2025-09-01 17:06:16.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:06:16.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:06:16.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:06:16.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:06:16.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:06:16.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:06:16.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:06:16.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:06:16.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:06:17.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:06:18.717 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:06:19.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:06:20.465 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:06:21.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:06:22.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:06:23.236 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:06:24.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:06:25.124 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:06:25.125 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 17:06:25.125 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 17:06:25.125 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:06:25.132 | 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: 7.17 ms

2025-09-01 17:06:25.133 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:06:25.215 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:06:25.307 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch541
2025-09-01 17:06:28.238 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 541/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, 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: 4.869e-05, size: 544, ETA: 0:19:42
2025-09-01 17:06:31.291 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 541/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, 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: 4.844e-05, size: 320, ETA: 0:19:39
2025-09-01 17:06:34.647 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 541/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.165s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.6, lr: 4.819e-05, size: 576, ETA: 0:19:36
2025-09-01 17:06:37.632 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 541/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.7, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.5, lr: 4.795e-05, size: 384, ETA: 0:19:33
2025-09-01 17:06:40.775 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 541/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, 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: 4.770e-05, size: 576, ETA: 0:19:30
2025-09-01 17:06:43.730 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 541/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, 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: 4.745e-05, size: 416, ETA: 0:19:27
2025-09-01 17:06:45.170 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:06:51.261 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:06:51.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:06:52.080 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5808
2025-09-01 17:06:52.146 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5126
2025-09-01 17:06:52.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3729
2025-09-01 17:06:52.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4888
2025-09-01 17:06:52.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:06:52.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:06:52.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.581
2025-09-01 17:06:52.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-09-01 17:06:52.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.373
2025-09-01 17:06:52.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.489
2025-09-01 17:06:52.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:06:52.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:06:52.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:06:52.218 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:06:52.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:06:52.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:06:52.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:06:52.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:06:52.219 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:06:52.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:06:53.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:06:53.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:06:53.804 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:06:54.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:06:54.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:06:54.986 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:06:55.375 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:06:55.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:06:55.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 17:06:55.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.49
2025-09-01 17:06:55.762 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:06:55.769 | 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

2025-09-01 17:06:55.771 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:06:55.857 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:06:55.936 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch542
2025-09-01 17:06:58.962 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 542/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.9, lr: 4.709e-05, size: 448, ETA: 0:19:22
2025-09-01 17:07:01.903 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 542/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.8, lr: 4.685e-05, size: 256, ETA: 0:19:19
2025-09-01 17:07:04.974 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 542/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.6, l1_loss: 0.7, conf_loss: 0.7, cls_loss: 0.5, lr: 4.660e-05, size: 384, ETA: 0:19:16
2025-09-01 17:07:08.069 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 542/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.7, lr: 4.636e-05, size: 448, ETA: 0:19:13
2025-09-01 17:07:11.090 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 542/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 3.3, cls_loss: 0.7, lr: 4.611e-05, size: 256, ETA: 0:19:10
2025-09-01 17:07:14.159 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 542/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 4.587e-05, size: 512, ETA: 0:19:07
2025-09-01 17:07:15.530 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:07:21.747 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:07:22.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:07:22.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6091
2025-09-01 17:07:23.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5293
2025-09-01 17:07:23.065 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3866
2025-09-01 17:07:23.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5083
2025-09-01 17:07:23.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:07:23.066 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:07:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-09-01 17:07:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-09-01 17:07:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-09-01 17:07:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.508
2025-09-01 17:07:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:07:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:07:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:07:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:07:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:07:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:07:23.067 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:07:23.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:07:23.068 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:07:23.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:07:24.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:07:25.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:07:25.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:07:26.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:07:26.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:07:27.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:07:28.132 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:07:28.712 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:07:28.713 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 17:07:28.713 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:07:28.713 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:07:28.720 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.16 ms, Average NMS time: 0.86 ms, Average inference time: 7.03 ms

2025-09-01 17:07:28.721 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:07:28.810 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:07:28.896 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch543
2025-09-01 17:07:31.791 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 543/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, 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: 4.552e-05, size: 320, ETA: 0:19:02
2025-09-01 17:07:34.717 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 543/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.6, lr: 4.528e-05, size: 384, ETA: 0:18:59
2025-09-01 17:07:37.762 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 543/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 4.503e-05, size: 256, ETA: 0:18:56
2025-09-01 17:07:40.755 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 543/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.8, lr: 4.479e-05, size: 320, ETA: 0:18:53
2025-09-01 17:07:43.844 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 543/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 3.1, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.5, lr: 4.455e-05, size: 320, ETA: 0:18:50
2025-09-01 17:07:46.877 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 543/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.8, cls_loss: 0.7, lr: 4.431e-05, size: 544, ETA: 0:18:47
2025-09-01 17:07:48.268 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:07:54.643 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:07:55.602 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:07:56.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6014
2025-09-01 17:07:56.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5237
2025-09-01 17:07:56.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3867
2025-09-01 17:07:56.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5039
2025-09-01 17:07:56.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:07:56.448 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:07:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.601
2025-09-01 17:07:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-09-01 17:07:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-09-01 17:07:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-09-01 17:07:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:07:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:07:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:07:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:07:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:07:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:07:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:07:56.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:07:56.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:07:57.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:07:58.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:07:59.154 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:07:59.992 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:08:00.861 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:08:01.692 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:08:02.558 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:08:03.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:08:04.244 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:08:04.244 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 17:08:04.244 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 17:08:04.244 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:08:04.252 | 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: 7.14 ms

2025-09-01 17:08:04.253 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:08:04.375 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:08:04.449 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch544
2025-09-01 17:08:07.346 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 544/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.8, lr: 4.397e-05, size: 416, ETA: 0:18:43
2025-09-01 17:08:10.385 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 544/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 4.373e-05, size: 256, ETA: 0:18:39
2025-09-01 17:08:13.453 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 544/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 4.349e-05, size: 384, ETA: 0:18:36
2025-09-01 17:08:16.608 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 544/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.7, lr: 4.326e-05, size: 416, ETA: 0:18:33
2025-09-01 17:08:19.580 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 544/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 1.0, lr: 4.302e-05, size: 320, ETA: 0:18:30
2025-09-01 17:08:22.678 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 544/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 1.3, conf_loss: 1.7, cls_loss: 0.7, lr: 4.279e-05, size: 576, ETA: 0:18:27
2025-09-01 17:08:24.071 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:08:30.207 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:08:30.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:08:31.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6184
2025-09-01 17:08:31.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5191
2025-09-01 17:08:31.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3574
2025-09-01 17:08:31.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4983
2025-09-01 17:08:31.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:08:31.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:08:31.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.618
2025-09-01 17:08:31.525 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.519
2025-09-01 17:08:31.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.357
2025-09-01 17:08:31.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.498
2025-09-01 17:08:31.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:08:31.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:08:31.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:08:31.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:08:31.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:08:31.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:08:31.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:08:31.526 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:08:31.527 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:08:32.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:08:32.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:08:33.286 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:08:33.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:08:34.410 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:08:35.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:08:35.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:08:36.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:08:36.705 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:08:36.705 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:08:36.705 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 17:08:36.705 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:08:36.712 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.88 ms, Average inference time: 7.14 ms

2025-09-01 17:08:36.713 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:08:36.802 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:08:36.883 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch545
2025-09-01 17:08:39.883 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 545/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.6, iou_loss: 1.8, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.8, lr: 4.245e-05, size: 544, ETA: 0:18:23
2025-09-01 17:08:42.852 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 545/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.6, lr: 4.221e-05, size: 352, ETA: 0:18:20
2025-09-01 17:08:45.938 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 545/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.005s, total_loss: 7.8, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.8, lr: 4.198e-05, size: 384, ETA: 0:18:17
2025-09-01 17:08:48.848 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 545/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.6, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.7, lr: 4.175e-05, size: 320, ETA: 0:18:14
2025-09-01 17:08:51.780 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 545/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.9, cls_loss: 0.6, lr: 4.152e-05, size: 320, ETA: 0:18:10
2025-09-01 17:08:54.798 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 545/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 4.128e-05, size: 320, ETA: 0:18:07
2025-09-01 17:08:56.121 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:09:02.596 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:09:03.542 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:09:04.255 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6146
2025-09-01 17:09:04.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5212
2025-09-01 17:09:04.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3965
2025-09-01 17:09:04.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5108
2025-09-01 17:09:04.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:09:04.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:09:04.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.615
2025-09-01 17:09:04.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 17:09:04.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.397
2025-09-01 17:09:04.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-09-01 17:09:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:09:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:09:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:09:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:09:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:09:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:09:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:09:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:09:04.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:09:05.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:09:06.124 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:09:06.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:09:07.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:09:08.668 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:09:09.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:09:10.386 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:09:11.253 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:09:12.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:09:12.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:09:12.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:09:12.083 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:09:12.091 | 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

2025-09-01 17:09:12.096 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:09:12.180 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:09:12.261 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch546
2025-09-01 17:09:15.071 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 546/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.139s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 4.095e-05, size: 512, ETA: 0:18:03
2025-09-01 17:09:18.310 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 546/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 9.5, iou_loss: 3.2, l1_loss: 1.6, conf_loss: 3.8, cls_loss: 0.9, lr: 4.072e-05, size: 512, ETA: 0:18:00
2025-09-01 17:09:21.328 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 546/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.005s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 4.049e-05, size: 384, ETA: 0:17:57
2025-09-01 17:09:24.336 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 546/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.9, lr: 4.026e-05, size: 544, ETA: 0:17:54
2025-09-01 17:09:27.425 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 546/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.6, lr: 4.004e-05, size: 512, ETA: 0:17:51
2025-09-01 17:09:30.480 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 546/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 3.981e-05, size: 416, ETA: 0:17:48
2025-09-01 17:09:31.844 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:09:38.015 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:09:38.773 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:09:39.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6100
2025-09-01 17:09:39.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5446
2025-09-01 17:09:39.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4010
2025-09-01 17:09:39.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5185
2025-09-01 17:09:39.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:09:39.433 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:09:39.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.610
2025-09-01 17:09:39.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.545
2025-09-01 17:09:39.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.401
2025-09-01 17:09:39.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.519
2025-09-01 17:09:39.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:09:39.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:09:39.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:09:39.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:09:39.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:09:39.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:09:39.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:09:39.434 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:09:39.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:09:40.121 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:09:40.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:09:41.388 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:09:42.075 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:09:42.715 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:09:43.361 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:09:44.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:09:44.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:09:45.379 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:09:45.379 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:09:45.379 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:09:45.379 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:09:45.387 | 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

2025-09-01 17:09:45.388 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:09:45.508 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:09:45.583 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch547
2025-09-01 17:09:48.467 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 547/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 3.3, cls_loss: 0.7, lr: 3.948e-05, size: 512, ETA: 0:17:43
2025-09-01 17:09:51.551 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 547/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.5, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.8, lr: 3.925e-05, size: 256, ETA: 0:17:40
2025-09-01 17:09:54.554 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 547/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 3.903e-05, size: 576, ETA: 0:17:37
2025-09-01 17:09:57.628 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 547/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 3.2, iou_loss: 1.3, l1_loss: 0.3, conf_loss: 1.2, cls_loss: 0.4, lr: 3.881e-05, size: 384, ETA: 0:17:34
2025-09-01 17:10:00.634 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 547/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.9, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 3.858e-05, size: 544, ETA: 0:17:31
2025-09-01 17:10:03.682 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 547/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.7, lr: 3.836e-05, size: 416, ETA: 0:17:28
2025-09-01 17:10:05.019 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:10:11.357 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:10:11.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:10:12.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5639
2025-09-01 17:10:12.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5130
2025-09-01 17:10:12.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3447
2025-09-01 17:10:12.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4739
2025-09-01 17:10:12.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:10:12.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:10:12.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.564
2025-09-01 17:10:12.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.513
2025-09-01 17:10:12.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.345
2025-09-01 17:10:12.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.474
2025-09-01 17:10:12.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:10:12.215 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:10:12.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:10:12.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:10:12.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:10:12.216 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:10:12.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:10:12.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:10:12.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:10:12.693 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:10:13.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:10:13.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:10:13.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:10:14.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:10:14.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:10:14.885 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:10:15.248 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:10:15.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:10:15.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 17:10:15.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.47
2025-09-01 17:10:15.618 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:10:15.624 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.40 ms, Average NMS time: 0.87 ms, Average inference time: 7.28 ms

2025-09-01 17:10:15.626 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:10:15.710 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:10:15.793 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch548
2025-09-01 17:10:18.675 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 548/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.6, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.5, lr: 3.804e-05, size: 512, ETA: 0:17:23
2025-09-01 17:10:21.808 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 548/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.7, lr: 3.782e-05, size: 544, ETA: 0:17:20
2025-09-01 17:10:24.878 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 548/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.1, cls_loss: 0.6, lr: 3.759e-05, size: 416, ETA: 0:17:17
2025-09-01 17:10:27.906 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 548/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.5, lr: 3.737e-05, size: 480, ETA: 0:17:14
2025-09-01 17:10:30.966 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 548/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, 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: 3.715e-05, size: 352, ETA: 0:17:11
2025-09-01 17:10:34.094 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 548/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.001s, total_loss: 5.4, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.5, lr: 3.694e-05, size: 512, ETA: 0:17:08
2025-09-01 17:10:35.476 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:10:41.866 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:10:42.580 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:10:43.002 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6119
2025-09-01 17:10:43.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5403
2025-09-01 17:10:43.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4136
2025-09-01 17:10:43.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5219
2025-09-01 17:10:43.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:10:43.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:10:43.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.612
2025-09-01 17:10:43.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.540
2025-09-01 17:10:43.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-09-01 17:10:43.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.522
2025-09-01 17:10:43.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:10:43.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:10:43.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:10:43.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:10:43.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:10:43.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:10:43.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:10:43.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:10:43.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:10:43.769 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:10:44.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:10:44.956 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:10:45.530 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:10:46.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:10:46.734 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:10:47.308 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:10:47.892 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:10:48.465 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:10:48.466 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:10:48.466 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:10:48.466 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:10:48.473 | 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

2025-09-01 17:10:48.474 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:10:48.610 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:10:48.683 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch549
2025-09-01 17:10:51.575 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 549/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 3.662e-05, size: 512, ETA: 0:17:04
2025-09-01 17:10:54.690 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 549/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, 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: 3.640e-05, size: 320, ETA: 0:17:00
2025-09-01 17:10:57.774 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 549/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.2, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.6, lr: 3.619e-05, size: 288, ETA: 0:16:57
2025-09-01 17:11:00.772 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 549/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.9, lr: 3.597e-05, size: 480, ETA: 0:16:54
2025-09-01 17:11:03.749 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 549/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.9, lr: 3.575e-05, size: 256, ETA: 0:16:51
2025-09-01 17:11:06.738 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 549/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, 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: 3.554e-05, size: 448, ETA: 0:16:48
2025-09-01 17:11:08.112 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:11:14.270 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:11:15.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:11:15.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6137
2025-09-01 17:11:15.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5326
2025-09-01 17:11:15.975 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4113
2025-09-01 17:11:15.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5192
2025-09-01 17:11:15.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:11:15.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:11:15.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.614
2025-09-01 17:11:15.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.533
2025-09-01 17:11:15.976 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.411
2025-09-01 17:11:15.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.519
2025-09-01 17:11:15.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:11:15.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:11:15.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:11:15.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:11:15.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:11:15.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:11:15.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:11:15.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:11:15.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:11:16.728 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:11:17.523 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:11:18.281 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:11:19.032 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:11:19.813 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:11:20.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:11:21.321 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:11:22.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:11:22.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:11:22.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:11:22.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:11:22.865 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:11:22.872 | 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

2025-09-01 17:11:22.876 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:11:22.961 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:11:23.043 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch550
2025-09-01 17:11:25.894 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 550/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.8, lr: 3.523e-05, size: 448, ETA: 0:16:44
2025-09-01 17:11:28.897 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 550/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 3.502e-05, size: 320, ETA: 0:16:41
2025-09-01 17:11:31.946 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 550/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 3.9, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.0, cls_loss: 0.5, lr: 3.480e-05, size: 352, ETA: 0:16:38
2025-09-01 17:11:35.108 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 550/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 3.459e-05, size: 320, ETA: 0:16:35
2025-09-01 17:11:38.204 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 550/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, 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: 3.438e-05, size: 512, ETA: 0:16:31
2025-09-01 17:11:41.256 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 550/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 3.417e-05, size: 256, ETA: 0:16:28
2025-09-01 17:11:42.563 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:11:48.878 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:11:49.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:11:50.581 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6167
2025-09-01 17:11:50.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5211
2025-09-01 17:11:50.775 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3985
2025-09-01 17:11:50.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5121
2025-09-01 17:11:50.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:11:50.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:11:50.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.617
2025-09-01 17:11:50.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.521
2025-09-01 17:11:50.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-09-01 17:11:50.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.512
2025-09-01 17:11:50.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:11:50.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:11:50.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:11:50.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:11:50.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:11:50.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:11:50.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:11:50.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:11:50.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:11:51.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:11:52.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:11:53.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:11:54.269 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:11:55.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:11:56.073 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:11:57.003 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:11:57.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:11:58.850 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:11:58.851 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:11:58.851 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:11:58.851 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:11:58.859 | 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

2025-09-01 17:11:58.862 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:11:58.942 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:11:59.028 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch551
2025-09-01 17:12:01.883 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 551/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 3.386e-05, size: 352, ETA: 0:16:24
2025-09-01 17:12:05.023 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 551/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, 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: 3.366e-05, size: 480, ETA: 0:16:21
2025-09-01 17:12:08.087 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 551/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 4.5, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.5, lr: 3.345e-05, size: 416, ETA: 0:16:18
2025-09-01 17:12:11.071 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 551/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, 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: 3.324e-05, size: 352, ETA: 0:16:15
2025-09-01 17:12:14.074 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 551/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.1, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.6, lr: 3.303e-05, size: 352, ETA: 0:16:12
2025-09-01 17:12:17.083 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 551/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 2.2, cls_loss: 0.6, lr: 3.283e-05, size: 512, ETA: 0:16:09
2025-09-01 17:12:18.390 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:12:24.674 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:12:25.484 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:12:26.061 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6008
2025-09-01 17:12:26.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5063
2025-09-01 17:12:26.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3985
2025-09-01 17:12:26.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5019
2025-09-01 17:12:26.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:12:26.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:12:26.197 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.601
2025-09-01 17:12:26.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-09-01 17:12:26.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.399
2025-09-01 17:12:26.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.502
2025-09-01 17:12:26.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:12:26.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:12:26.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:12:26.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:12:26.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:12:26.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:12:26.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:12:26.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:12:26.198 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:12:26.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:12:27.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:12:28.389 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:12:29.133 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:12:29.851 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:12:30.536 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:12:31.261 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:12:31.957 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:12:32.684 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:12:32.684 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 17:12:32.685 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 17:12:32.685 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:12:32.692 | 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: 7.15 ms

2025-09-01 17:12:32.693 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:12:32.796 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:12:32.918 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch552
2025-09-01 17:12:35.833 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 552/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.144s, 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: 3.253e-05, size: 448, ETA: 0:16:04
2025-09-01 17:12:38.873 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 552/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.4, cls_loss: 0.8, lr: 3.232e-05, size: 544, ETA: 0:16:01
2025-09-01 17:12:41.865 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 552/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, 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: 3.212e-05, size: 288, ETA: 0:15:58
2025-09-01 17:12:44.967 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 552/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, 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: 3.191e-05, size: 576, ETA: 0:15:55
2025-09-01 17:12:48.020 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 552/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 1.3, conf_loss: 2.7, cls_loss: 0.7, lr: 3.171e-05, size: 320, ETA: 0:15:52
2025-09-01 17:12:50.988 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 552/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.5, lr: 3.151e-05, size: 512, ETA: 0:15:49
2025-09-01 17:12:52.311 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:12:58.470 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:12:59.503 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:13:00.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6074
2025-09-01 17:13:00.363 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5318
2025-09-01 17:13:00.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3690
2025-09-01 17:13:00.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5027
2025-09-01 17:13:00.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:13:00.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:13:00.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.607
2025-09-01 17:13:00.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-09-01 17:13:00.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-09-01 17:13:00.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-09-01 17:13:00.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:13:00.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:13:00.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:13:00.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:13:00.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:13:00.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:13:00.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:13:00.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:13:00.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:13:01.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:13:02.189 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:13:03.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:13:03.987 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:13:04.870 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:13:05.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:13:06.592 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:13:07.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:13:08.410 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:13:08.410 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:13:08.410 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 17:13:08.411 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:13:08.418 | 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

2025-09-01 17:13:08.419 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:13:08.499 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:13:08.629 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch553
2025-09-01 17:13:11.555 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 553/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.6, lr: 3.122e-05, size: 448, ETA: 0:15:44
2025-09-01 17:13:14.595 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 553/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 3.101e-05, size: 512, ETA: 0:15:41
2025-09-01 17:13:17.700 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 553/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.7, cls_loss: 0.9, lr: 3.081e-05, size: 352, ETA: 0:15:38
2025-09-01 17:13:20.677 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 553/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.7, lr: 3.061e-05, size: 320, ETA: 0:15:35
2025-09-01 17:13:23.695 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 553/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.6, lr: 3.042e-05, size: 352, ETA: 0:15:32
2025-09-01 17:13:26.623 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 553/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 3.9, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.5, lr: 3.022e-05, size: 416, ETA: 0:15:29
2025-09-01 17:13:27.945 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:13:33.993 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:13:35.017 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:13:35.702 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6075
2025-09-01 17:13:35.812 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5250
2025-09-01 17:13:35.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4020
2025-09-01 17:13:35.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5115
2025-09-01 17:13:35.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:13:35.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:13:35.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.608
2025-09-01 17:13:35.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-09-01 17:13:35.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-09-01 17:13:35.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.512
2025-09-01 17:13:35.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:13:35.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:13:35.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:13:35.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:13:35.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:13:35.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:13:35.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:13:35.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:13:35.899 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:13:36.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:13:37.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:13:38.418 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:13:39.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:13:40.087 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:13:40.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:13:41.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:13:42.611 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:13:43.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:13:43.455 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:13:43.456 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:13:43.456 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:13:43.463 | 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: 7.24 ms

2025-09-01 17:13:43.464 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:13:43.544 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:13:43.674 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch554
2025-09-01 17:13:46.535 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 554/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.7, cls_loss: 0.6, lr: 2.993e-05, size: 352, ETA: 0:15:25
2025-09-01 17:13:49.615 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 554/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 2.973e-05, size: 320, ETA: 0:15:21
2025-09-01 17:13:52.588 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 554/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, 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: 2.954e-05, size: 384, ETA: 0:15:18
2025-09-01 17:13:55.472 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 554/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 2.934e-05, size: 320, ETA: 0:15:15
2025-09-01 17:13:58.454 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 554/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.1, cls_loss: 0.7, lr: 2.915e-05, size: 288, ETA: 0:15:12
2025-09-01 17:14:01.434 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 554/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 1.6, cls_loss: 0.8, lr: 2.895e-05, size: 512, ETA: 0:15:09
2025-09-01 17:14:02.781 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:14:08.973 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:14:09.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:14:10.256 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6196
2025-09-01 17:14:10.390 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5232
2025-09-01 17:14:10.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4142
2025-09-01 17:14:10.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5190
2025-09-01 17:14:10.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:14:10.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:14:10.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.620
2025-09-01 17:14:10.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-09-01 17:14:10.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-09-01 17:14:10.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.519
2025-09-01 17:14:10.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:14:10.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:14:10.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:14:10.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:14:10.425 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:14:10.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:14:10.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:14:10.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:14:10.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:14:11.063 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:14:11.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:14:12.441 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:14:13.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:14:13.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:14:14.409 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:14:15.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:14:15.679 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:14:16.360 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:14:16.360 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:14:16.360 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:14:16.361 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:14:16.367 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.21 ms, Average NMS time: 0.87 ms, Average inference time: 7.08 ms

2025-09-01 17:14:16.369 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:14:16.466 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:14:16.546 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch555
2025-09-01 17:14:19.536 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 555/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.7, lr: 2.867e-05, size: 512, ETA: 0:15:05
2025-09-01 17:14:22.538 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 555/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.6, lr: 2.848e-05, size: 544, ETA: 0:15:02
2025-09-01 17:14:25.623 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 555/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 1.1, conf_loss: 2.0, cls_loss: 0.6, lr: 2.829e-05, size: 576, ETA: 0:14:59
2025-09-01 17:14:28.686 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 555/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.1, iou_loss: 1.2, l1_loss: 0.5, conf_loss: 3.0, cls_loss: 0.4, lr: 2.810e-05, size: 544, ETA: 0:14:56
2025-09-01 17:14:31.729 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 555/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.7, lr: 2.791e-05, size: 320, ETA: 0:14:52
2025-09-01 17:14:34.778 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 555/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, 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: 2.772e-05, size: 512, ETA: 0:14:49
2025-09-01 17:14:36.099 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:14:42.228 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:14:43.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:14:44.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5957
2025-09-01 17:14:44.480 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5404
2025-09-01 17:14:44.517 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3900
2025-09-01 17:14:44.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5087
2025-09-01 17:14:44.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:14:44.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:14:44.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.596
2025-09-01 17:14:44.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.540
2025-09-01 17:14:44.518 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.390
2025-09-01 17:14:44.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.509
2025-09-01 17:14:44.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:14:44.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:14:44.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:14:44.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:14:44.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:14:44.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:14:44.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:14:44.519 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:14:44.520 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:14:45.574 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:14:46.625 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:14:47.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:14:48.726 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:14:49.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:14:50.840 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:14:51.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:14:52.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:14:53.971 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:14:53.972 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:14:53.972 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:14:53.972 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:14:53.979 | 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

2025-09-01 17:14:53.984 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:14:54.110 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:14:54.231 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch556
2025-09-01 17:14:57.178 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 556/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, 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: 2.744e-05, size: 512, ETA: 0:14:45
2025-09-01 17:15:00.212 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 556/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 2.725e-05, size: 448, ETA: 0:14:42
2025-09-01 17:15:03.274 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 556/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.6, lr: 2.706e-05, size: 544, ETA: 0:14:39
2025-09-01 17:15:06.258 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 556/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.7, lr: 2.688e-05, size: 352, ETA: 0:14:36
2025-09-01 17:15:09.400 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 556/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 7.1, iou_loss: 2.7, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 2.669e-05, size: 320, ETA: 0:14:33
2025-09-01 17:15:12.500 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 556/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.8, cls_loss: 1.0, lr: 2.650e-05, size: 256, ETA: 0:14:30
2025-09-01 17:15:13.792 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:15:19.999 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:15:20.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:15:21.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6104
2025-09-01 17:15:21.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5370
2025-09-01 17:15:21.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3961
2025-09-01 17:15:21.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5145
2025-09-01 17:15:21.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:15:21.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:15:21.816 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.610
2025-09-01 17:15:21.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.537
2025-09-01 17:15:21.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.396
2025-09-01 17:15:21.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-09-01 17:15:21.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:15:21.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:15:21.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:15:21.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:15:21.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:15:21.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:15:21.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:15:21.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:15:21.817 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:15:22.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:15:23.551 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:15:24.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:15:25.323 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:15:26.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:15:27.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:15:27.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:15:28.762 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:15:29.605 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:15:29.605 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:15:29.606 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:15:29.606 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:15:29.613 | 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

2025-09-01 17:15:29.617 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:15:29.703 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:15:29.795 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch557
2025-09-01 17:15:32.753 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 557/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 2.624e-05, size: 512, ETA: 0:14:25
2025-09-01 17:15:35.775 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 557/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.4, cls_loss: 0.8, lr: 2.605e-05, size: 448, ETA: 0:14:22
2025-09-01 17:15:38.854 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 557/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.006s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.2, conf_loss: 2.9, cls_loss: 0.7, lr: 2.587e-05, size: 448, ETA: 0:14:19
2025-09-01 17:15:41.831 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 557/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 2.3, cls_loss: 0.7, lr: 2.568e-05, size: 416, ETA: 0:14:16
2025-09-01 17:15:45.015 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 557/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 2.550e-05, size: 256, ETA: 0:14:13
2025-09-01 17:15:47.942 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 557/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.5, lr: 2.532e-05, size: 352, ETA: 0:14:10
2025-09-01 17:15:49.329 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:15:55.692 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:15:56.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:15:57.195 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5986
2025-09-01 17:15:57.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5235
2025-09-01 17:15:57.406 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3887
2025-09-01 17:15:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5036
2025-09-01 17:15:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:15:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:15:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.599
2025-09-01 17:15:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-09-01 17:15:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.389
2025-09-01 17:15:57.407 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.504
2025-09-01 17:15:57.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:15:57.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:15:57.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:15:57.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:15:57.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:15:57.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:15:57.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:15:57.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:15:57.408 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:15:58.166 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:15:58.983 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:15:59.743 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:16:00.552 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:16:01.313 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:16:02.076 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:16:02.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:16:03.649 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:16:04.449 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:16:04.449 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 17:16:04.449 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 17:16:04.449 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:16:04.457 | 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

2025-09-01 17:16:04.457 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:16:04.544 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:16:04.626 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch558
2025-09-01 17:16:07.523 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 558/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.6, cls_loss: 0.9, lr: 2.506e-05, size: 288, ETA: 0:14:05
2025-09-01 17:16:10.480 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 558/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 2.3, cls_loss: 0.8, lr: 2.488e-05, size: 384, ETA: 0:14:02
2025-09-01 17:16:13.567 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 558/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 7.3, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.7, lr: 2.470e-05, size: 352, ETA: 0:13:59
2025-09-01 17:16:16.512 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 558/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.1, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 2.7, cls_loss: 1.0, lr: 2.452e-05, size: 416, ETA: 0:13:56
2025-09-01 17:16:19.565 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 558/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.4, iou_loss: 1.9, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 1.0, lr: 2.434e-05, size: 576, ETA: 0:13:53
2025-09-01 17:16:22.675 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 558/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, 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: 2.416e-05, size: 480, ETA: 0:13:50
2025-09-01 17:16:24.061 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:16:30.195 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:16:31.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:16:31.496 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6164
2025-09-01 17:16:31.653 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5491
2025-09-01 17:16:31.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4091
2025-09-01 17:16:31.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5249
2025-09-01 17:16:31.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:16:31.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:16:31.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.616
2025-09-01 17:16:31.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.549
2025-09-01 17:16:31.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-09-01 17:16:31.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.525
2025-09-01 17:16:31.696 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:16:31.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:16:31.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:16:31.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:16:31.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:16:31.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:16:31.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:16:31.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:16:31.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:16:32.499 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:16:33.190 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:16:33.849 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:16:34.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:16:35.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:16:35.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:16:36.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:16:37.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:16:37.893 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:16:37.894 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:16:37.894 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:16:37.894 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:16:37.905 | 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

2025-09-01 17:16:37.906 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:16:37.996 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:16:38.077 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch559
2025-09-01 17:16:40.835 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 559/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.136s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.6, lr: 2.391e-05, size: 384, ETA: 0:13:46
2025-09-01 17:16:43.905 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 559/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.7, iou_loss: 3.0, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 2.373e-05, size: 352, ETA: 0:13:42
2025-09-01 17:16:46.929 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 559/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.005s, total_loss: 4.6, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 2.355e-05, size: 384, ETA: 0:13:39
2025-09-01 17:16:50.023 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 559/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, 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: 2.338e-05, size: 576, ETA: 0:13:36
2025-09-01 17:16:53.071 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 559/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, 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: 2.321e-05, size: 384, ETA: 0:13:33
2025-09-01 17:16:56.082 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 559/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 10.3, iou_loss: 3.3, l1_loss: 1.2, conf_loss: 5.0, cls_loss: 0.8, lr: 2.303e-05, size: 256, ETA: 0:13:30
2025-09-01 17:16:57.431 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:17:03.622 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:17:04.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:17:05.180 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6189
2025-09-01 17:17:05.303 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5165
2025-09-01 17:17:05.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4034
2025-09-01 17:17:05.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5129
2025-09-01 17:17:05.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:17:05.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:17:05.383 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.619
2025-09-01 17:17:05.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.516
2025-09-01 17:17:05.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-09-01 17:17:05.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-09-01 17:17:05.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:17:05.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:17:05.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:17:05.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:17:05.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:17:05.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:17:05.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:17:05.384 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:17:05.385 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:17:06.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:17:06.850 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:17:07.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:17:08.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:17:09.126 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:17:09.848 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:17:10.607 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:17:11.330 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:17:12.073 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:17:12.074 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:17:12.074 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:17:12.074 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:17:12.081 | 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

2025-09-01 17:17:12.085 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:17:12.179 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:17:12.297 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch560
2025-09-01 17:17:15.116 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 560/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.4, cls_loss: 0.6, lr: 2.278e-05, size: 256, ETA: 0:13:26
2025-09-01 17:17:18.076 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 560/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, 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: 352, ETA: 0:13:23
2025-09-01 17:17:21.000 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 560/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.003s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 2.244e-05, size: 448, ETA: 0:13:20
2025-09-01 17:17:24.080 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 560/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, 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: 2.227e-05, size: 256, ETA: 0:13:17
2025-09-01 17:17:26.965 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 560/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 4.8, iou_loss: 1.7, l1_loss: 0.7, conf_loss: 1.9, cls_loss: 0.6, lr: 2.210e-05, size: 512, ETA: 0:13:13
2025-09-01 17:17:29.998 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 560/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.3, iou_loss: 2.8, l1_loss: 1.2, conf_loss: 2.5, cls_loss: 0.7, lr: 2.193e-05, size: 448, ETA: 0:13:10
2025-09-01 17:17:31.401 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:17:37.461 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:17:38.171 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:17:38.657 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6186
2025-09-01 17:17:38.756 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5355
2025-09-01 17:17:38.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4085
2025-09-01 17:17:38.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5209
2025-09-01 17:17:38.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:17:38.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:17:38.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.619
2025-09-01 17:17:38.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-09-01 17:17:38.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-09-01 17:17:38.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.521
2025-09-01 17:17:38.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:17:38.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:17:38.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:17:38.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:17:38.827 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:17:38.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:17:38.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:17:38.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:17:38.828 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:17:39.678 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:17:40.295 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:17:40.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:17:41.537 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:17:42.150 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:17:42.757 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:17:43.371 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:17:44.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:17:44.635 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:17:44.635 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:17:44.635 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:17:44.635 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:17:44.645 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.28 ms, Average NMS time: 0.88 ms, Average inference time: 7.15 ms

2025-09-01 17:17:44.649 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:17:44.735 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:17:44.817 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch561
2025-09-01 17:17:47.659 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 561/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, 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: 2.168e-05, size: 576, ETA: 0:13:06
2025-09-01 17:17:50.645 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 561/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, 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: 2.152e-05, size: 416, ETA: 0:13:03
2025-09-01 17:17:53.829 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 561/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.158s, data_time: 0.003s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 2.135e-05, size: 320, ETA: 0:13:00
2025-09-01 17:17:56.970 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 561/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.5, lr: 2.118e-05, size: 576, ETA: 0:12:57
2025-09-01 17:18:00.045 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 561/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 2.7, cls_loss: 0.7, lr: 2.102e-05, size: 480, ETA: 0:12:54
2025-09-01 17:18:03.015 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 561/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 7.8, iou_loss: 3.1, l1_loss: 1.1, conf_loss: 2.9, cls_loss: 0.7, lr: 2.085e-05, size: 320, ETA: 0:12:51
2025-09-01 17:18:04.353 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:18:10.574 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:18:11.469 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:18:12.072 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6251
2025-09-01 17:18:12.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5389
2025-09-01 17:18:12.228 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4000
2025-09-01 17:18:12.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5213
2025-09-01 17:18:12.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:18:12.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:18:12.229 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.625
2025-09-01 17:18:12.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.539
2025-09-01 17:18:12.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-09-01 17:18:12.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.521
2025-09-01 17:18:12.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:18:12.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:18:12.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:18:12.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:18:12.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:18:12.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:18:12.230 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:18:12.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:18:12.231 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:18:13.016 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:18:13.758 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:18:14.532 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:18:15.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:18:16.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:18:16.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:18:17.514 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:18:18.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:18:19.021 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:18:19.022 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:18:19.022 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:18:19.022 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:18:19.029 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.93 ms, Average inference time: 7.19 ms

2025-09-01 17:18:19.031 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:18:19.118 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:18:19.200 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch562
2025-09-01 17:18:22.238 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 562/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 2.4, cls_loss: 0.9, lr: 2.061e-05, size: 576, ETA: 0:12:46
2025-09-01 17:18:25.265 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 562/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 2.045e-05, size: 320, ETA: 0:12:43
2025-09-01 17:18:28.371 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 562/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.006s, total_loss: 5.3, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.7, lr: 2.029e-05, size: 576, ETA: 0:12:40
2025-09-01 17:18:31.424 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 562/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.9, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.7, lr: 2.012e-05, size: 512, ETA: 0:12:37
2025-09-01 17:18:34.424 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 562/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.6, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.5, lr: 1.996e-05, size: 480, ETA: 0:12:34
2025-09-01 17:18:37.587 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 562/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 3.1, iou_loss: 1.6, l1_loss: 0.4, conf_loss: 0.6, cls_loss: 0.5, lr: 1.980e-05, size: 352, ETA: 0:12:31
2025-09-01 17:18:39.084 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:18:45.343 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:18:46.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:18:46.916 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6147
2025-09-01 17:18:47.036 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5382
2025-09-01 17:18:47.115 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3830
2025-09-01 17:18:47.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5120
2025-09-01 17:18:47.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:18:47.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:18:47.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.615
2025-09-01 17:18:47.116 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.538
2025-09-01 17:18:47.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-09-01 17:18:47.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.512
2025-09-01 17:18:47.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:18:47.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:18:47.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:18:47.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:18:47.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:18:47.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:18:47.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:18:47.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:18:47.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:18:47.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:18:48.752 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:18:49.556 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:18:50.382 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:18:51.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:18:52.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:18:52.808 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:18:53.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:18:54.401 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:18:54.401 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:18:54.401 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:18:54.401 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:18:54.409 | 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: 7.30 ms

2025-09-01 17:18:54.410 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:18:54.497 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:18:54.579 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch563
2025-09-01 17:18:57.619 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 563/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 3.2, iou_loss: 1.3, l1_loss: 0.6, conf_loss: 0.8, cls_loss: 0.4, lr: 1.957e-05, size: 512, ETA: 0:12:26
2025-09-01 17:19:00.838 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 563/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.160s, data_time: 0.001s, total_loss: 5.1, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.7, cls_loss: 0.6, lr: 1.941e-05, size: 416, ETA: 0:12:23
2025-09-01 17:19:04.090 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 563/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.159s, data_time: 0.004s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.7, lr: 1.925e-05, size: 256, ETA: 0:12:20
2025-09-01 17:19:07.160 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 563/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.005s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 1.909e-05, size: 384, ETA: 0:12:17
2025-09-01 17:19:10.377 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 563/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.159s, data_time: 0.001s, total_loss: 10.0, iou_loss: 3.5, l1_loss: 1.3, conf_loss: 4.4, cls_loss: 0.8, lr: 1.893e-05, size: 448, ETA: 0:12:14
2025-09-01 17:19:13.572 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 563/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.159s, 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: 1.878e-05, size: 576, ETA: 0:12:11
2025-09-01 17:19:15.016 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:19:21.209 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:19:22.143 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:19:22.753 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6041
2025-09-01 17:19:22.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5259
2025-09-01 17:19:23.007 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3919
2025-09-01 17:19:23.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5073
2025-09-01 17:19:23.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:19:23.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:19:23.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.604
2025-09-01 17:19:23.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.526
2025-09-01 17:19:23.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.392
2025-09-01 17:19:23.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.507
2025-09-01 17:19:23.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:19:23.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:19:23.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:19:23.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:19:23.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:19:23.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:19:23.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:19:23.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:19:23.012 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:19:23.857 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:19:24.659 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:19:25.436 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:19:26.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:19:27.182 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:19:27.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:19:28.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:19:29.666 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:19:30.468 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:19:30.468 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.24
2025-09-01 17:19:30.468 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:19:30.468 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:19:30.476 | 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

2025-09-01 17:19:30.479 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:19:30.565 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:19:30.647 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch564
2025-09-01 17:19:33.629 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 564/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.3, l1_loss: 1.1, conf_loss: 2.8, cls_loss: 0.6, lr: 1.855e-05, size: 576, ETA: 0:12:07
2025-09-01 17:19:36.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 564/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.6, lr: 1.840e-05, size: 480, ETA: 0:12:04
2025-09-01 17:19:39.664 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 564/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.003s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.6, lr: 1.824e-05, size: 544, ETA: 0:12:01
2025-09-01 17:19:42.781 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 564/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 0.9, cls_loss: 0.5, lr: 1.809e-05, size: 512, ETA: 0:11:58
2025-09-01 17:19:45.892 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 564/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.5, l1_loss: 0.4, conf_loss: 1.1, cls_loss: 0.5, lr: 1.793e-05, size: 320, ETA: 0:11:55
2025-09-01 17:19:48.983 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 564/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, 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: 1.778e-05, size: 288, ETA: 0:11:51
2025-09-01 17:19:50.294 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:19:56.651 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:19:57.460 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:19:58.037 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6186
2025-09-01 17:19:58.137 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5320
2025-09-01 17:19:58.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3774
2025-09-01 17:19:58.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5093
2025-09-01 17:19:58.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:19:58.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:19:58.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.619
2025-09-01 17:19:58.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-09-01 17:19:58.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-09-01 17:19:58.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.509
2025-09-01 17:19:58.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:19:58.174 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:19:58.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:19:58.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:19:58.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:19:58.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:19:58.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:19:58.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:19:58.175 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:19:58.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:19:59.628 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:20:00.325 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:20:01.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:20:01.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:20:02.450 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:20:03.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:20:03.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:20:04.626 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:20:04.626 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:20:04.626 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:20:04.627 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:20:04.638 | 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: 7.24 ms

2025-09-01 17:20:04.640 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:20:04.760 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:20:04.878 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch565
2025-09-01 17:20:07.767 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 565/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.756e-05, size: 320, ETA: 0:11:47
2025-09-01 17:20:10.854 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 565/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.6, lr: 1.741e-05, size: 576, ETA: 0:11:44
2025-09-01 17:20:13.954 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 565/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, 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: 1.726e-05, size: 480, ETA: 0:11:41
2025-09-01 17:20:16.979 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 565/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, 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: 1.711e-05, size: 480, ETA: 0:11:38
2025-09-01 17:20:20.037 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 565/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, 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: 1.696e-05, size: 576, ETA: 0:11:35
2025-09-01 17:20:23.102 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 565/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.5, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 2.0, cls_loss: 0.6, lr: 1.681e-05, size: 576, ETA: 0:11:32
2025-09-01 17:20:24.572 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:20:30.673 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:20:31.405 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:20:31.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6147
2025-09-01 17:20:32.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5284
2025-09-01 17:20:32.094 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3978
2025-09-01 17:20:32.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5136
2025-09-01 17:20:32.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:20:32.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:20:32.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.615
2025-09-01 17:20:32.095 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-09-01 17:20:32.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.398
2025-09-01 17:20:32.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.514
2025-09-01 17:20:32.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:20:32.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:20:32.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:20:32.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:20:32.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:20:32.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:20:32.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:20:32.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:20:32.097 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:20:32.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:20:33.374 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:20:34.034 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:20:34.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:20:35.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:20:36.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:20:36.716 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:20:37.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:20:37.977 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:20:37.977 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:20:37.978 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:20:37.978 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:20:37.985 | 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

2025-09-01 17:20:37.986 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:20:38.066 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:20:38.202 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch566
2025-09-01 17:20:41.085 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 566/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 1.660e-05, size: 576, ETA: 0:11:27
2025-09-01 17:20:44.169 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 566/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.5, lr: 1.645e-05, size: 544, ETA: 0:11:24
2025-09-01 17:20:47.282 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 566/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, 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: 1.630e-05, size: 288, ETA: 0:11:21
2025-09-01 17:20:50.351 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 566/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 1.616e-05, size: 320, ETA: 0:11:18
2025-09-01 17:20:53.343 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 566/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 1.8, cls_loss: 0.6, lr: 1.601e-05, size: 544, ETA: 0:11:15
2025-09-01 17:20:56.403 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 566/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.8, cls_loss: 0.5, lr: 1.587e-05, size: 416, ETA: 0:11:12
2025-09-01 17:20:57.836 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:21:03.927 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:21:04.572 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:21:05.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6137
2025-09-01 17:21:05.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5458
2025-09-01 17:21:05.147 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4001
2025-09-01 17:21:05.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5199
2025-09-01 17:21:05.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:21:05.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:21:05.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.614
2025-09-01 17:21:05.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.546
2025-09-01 17:21:05.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.400
2025-09-01 17:21:05.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.520
2025-09-01 17:21:05.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:21:05.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:21:05.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:21:05.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:21:05.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:21:05.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:21:05.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:21:05.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:21:05.149 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:21:05.781 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:21:06.348 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:21:06.863 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:21:07.379 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:21:07.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:21:08.420 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:21:08.970 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:21:09.492 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:21:10.008 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:21:10.009 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:21:10.009 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:21:10.009 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:21:10.016 | 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

2025-09-01 17:21:10.017 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:21:10.106 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:21:10.188 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch567
2025-09-01 17:21:13.234 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 567/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 1.566e-05, size: 480, ETA: 0:11:07
2025-09-01 17:21:16.180 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 567/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.5, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.9, cls_loss: 0.7, lr: 1.552e-05, size: 544, ETA: 0:11:04
2025-09-01 17:21:19.180 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 567/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, 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: 1.538e-05, size: 480, ETA: 0:11:01
2025-09-01 17:21:22.310 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 567/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 6.6, iou_loss: 2.2, l1_loss: 1.2, conf_loss: 2.6, cls_loss: 0.6, lr: 1.523e-05, size: 544, ETA: 0:10:58
2025-09-01 17:21:25.390 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 567/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.6, lr: 1.509e-05, size: 384, ETA: 0:10:55
2025-09-01 17:21:28.419 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 567/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, 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: 1.495e-05, size: 288, ETA: 0:10:52
2025-09-01 17:21:29.704 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:21:35.845 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:21:36.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:21:36.998 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6086
2025-09-01 17:21:37.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5240
2025-09-01 17:21:37.161 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4125
2025-09-01 17:21:37.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5150
2025-09-01 17:21:37.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:21:37.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:21:37.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.609
2025-09-01 17:21:37.162 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.524
2025-09-01 17:21:37.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-09-01 17:21:37.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.515
2025-09-01 17:21:37.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:21:37.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:21:37.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:21:37.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:21:37.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:21:37.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:21:37.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:21:37.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:21:37.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:21:37.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:21:38.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:21:39.019 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:21:39.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:21:40.223 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:21:40.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:21:41.474 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:21:42.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:21:42.691 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:21:42.691 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:21:42.691 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:21:42.691 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:21:42.698 | 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

2025-09-01 17:21:42.700 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:21:42.836 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:21:42.909 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch568
2025-09-01 17:21:45.796 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 568/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.7, lr: 1.475e-05, size: 576, ETA: 0:10:48
2025-09-01 17:21:48.960 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 568/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.6, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.5, lr: 1.461e-05, size: 448, ETA: 0:10:45
2025-09-01 17:21:52.078 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 568/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.5, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 2.7, cls_loss: 0.7, lr: 1.447e-05, size: 352, ETA: 0:10:42
2025-09-01 17:21:55.075 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 568/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 3.6, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.6, lr: 1.434e-05, size: 448, ETA: 0:10:39
2025-09-01 17:21:58.242 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 568/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 1.8, cls_loss: 1.2, lr: 1.420e-05, size: 576, ETA: 0:10:35
2025-09-01 17:22:01.246 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 568/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.2, l1_loss: 0.6, conf_loss: 2.4, cls_loss: 0.6, lr: 1.406e-05, size: 480, ETA: 0:10:32
2025-09-01 17:22:02.604 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:22:08.928 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:22:09.747 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:22:10.344 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6211
2025-09-01 17:22:10.438 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5415
2025-09-01 17:22:10.511 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3914
2025-09-01 17:22:10.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5180
2025-09-01 17:22:10.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:22:10.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:22:10.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.621
2025-09-01 17:22:10.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-09-01 17:22:10.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.391
2025-09-01 17:22:10.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.518
2025-09-01 17:22:10.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:22:10.512 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:22:10.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:22:10.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:22:10.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:22:10.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:22:10.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:22:10.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:22:10.513 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:22:11.237 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:22:11.944 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:22:12.699 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:22:13.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:22:14.170 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:22:14.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:22:15.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:22:16.350 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:22:17.056 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:22:17.056 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:22:17.056 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:22:17.056 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:22:17.063 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.90 ms, Average inference time: 7.19 ms

2025-09-01 17:22:17.064 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:22:17.152 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:22:17.234 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch569
2025-09-01 17:22:20.129 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 569/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.1, cls_loss: 0.6, lr: 1.387e-05, size: 416, ETA: 0:10:28
2025-09-01 17:22:23.125 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 569/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.7, lr: 1.373e-05, size: 352, ETA: 0:10:25
2025-09-01 17:22:26.154 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 569/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 3.6, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.6, cls_loss: 0.5, lr: 1.360e-05, size: 480, ETA: 0:10:22
2025-09-01 17:22:29.375 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 569/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.159s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.5, lr: 1.347e-05, size: 384, ETA: 0:10:19
2025-09-01 17:22:32.427 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 569/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.334e-05, size: 576, ETA: 0:10:16
2025-09-01 17:22:35.532 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 569/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.7, iou_loss: 3.0, l1_loss: 0.9, conf_loss: 2.0, cls_loss: 0.7, lr: 1.320e-05, size: 256, ETA: 0:10:13
2025-09-01 17:22:36.912 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:22:43.507 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:22:44.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:22:45.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6132
2025-09-01 17:22:46.163 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5349
2025-09-01 17:22:46.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3867
2025-09-01 17:22:46.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5116
2025-09-01 17:22:46.200 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:22:46.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:22:46.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.613
2025-09-01 17:22:46.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-09-01 17:22:46.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.387
2025-09-01 17:22:46.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.512
2025-09-01 17:22:46.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:22:46.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:22:46.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:22:46.201 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:22:46.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:22:46.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:22:46.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:22:46.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:22:46.202 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:22:47.453 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:22:48.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:22:49.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:22:51.096 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:22:52.311 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:22:53.524 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:22:54.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:22:55.950 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:22:57.145 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:22:57.145 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:22:57.146 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:22:57.146 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:22:57.153 | 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

2025-09-01 17:22:57.154 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:22:57.233 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:22:57.318 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch570
2025-09-01 17:23:00.384 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 570/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 2.1, cls_loss: 0.7, lr: 1.301e-05, size: 320, ETA: 0:10:08
2025-09-01 17:23:03.303 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 570/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.8, cls_loss: 0.6, lr: 1.288e-05, size: 352, ETA: 0:10:05
2025-09-01 17:23:06.328 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 570/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.005s, total_loss: 4.5, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.5, lr: 1.275e-05, size: 544, ETA: 0:10:02
2025-09-01 17:23:09.316 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 570/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.148s, 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: 1.262e-05, size: 480, ETA: 0:09:59
2025-09-01 17:23:12.328 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 570/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.8, cls_loss: 0.7, lr: 1.250e-05, size: 288, ETA: 0:09:56
2025-09-01 17:23:15.395 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 570/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 8.9, iou_loss: 3.4, l1_loss: 1.3, conf_loss: 3.5, cls_loss: 0.7, lr: 1.237e-05, size: 256, ETA: 0:09:53
2025-09-01 17:23:16.707 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:23:22.831 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:23:23.921 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:23:24.720 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6168
2025-09-01 17:23:24.859 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5170
2025-09-01 17:23:24.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4054
2025-09-01 17:23:24.896 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5131
2025-09-01 17:23:24.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:23:24.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:23:24.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.617
2025-09-01 17:23:24.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.517
2025-09-01 17:23:24.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.405
2025-09-01 17:23:24.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-09-01 17:23:24.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:23:24.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:23:24.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:23:24.897 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:23:24.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:23:24.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:23:24.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:23:24.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:23:24.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:23:25.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:23:26.879 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:23:27.841 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:23:28.750 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:23:29.704 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:23:30.648 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:23:31.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:23:32.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:23:33.521 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:23:33.522 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:23:33.522 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:23:33.522 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:23:33.529 | 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: 7.22 ms

2025-09-01 17:23:33.530 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:23:33.615 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:23:33.699 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch571
2025-09-01 17:23:36.615 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 571/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 1.218e-05, size: 448, ETA: 0:09:48
2025-09-01 17:23:39.617 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 571/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.3, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.6, lr: 1.206e-05, size: 448, ETA: 0:09:45
2025-09-01 17:23:42.647 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 571/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 1.7, cls_loss: 0.8, lr: 1.193e-05, size: 288, ETA: 0:09:42
2025-09-01 17:23:45.789 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 571/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 4.8, iou_loss: 1.9, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.5, lr: 1.181e-05, size: 352, ETA: 0:09:39
2025-09-01 17:23:48.787 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 571/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 1.9, cls_loss: 0.8, lr: 1.168e-05, size: 576, ETA: 0:09:36
2025-09-01 17:23:51.921 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 571/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.0, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.156e-05, size: 288, ETA: 0:09:33
2025-09-01 17:23:53.355 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:23:59.647 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:24:00.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:24:00.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6167
2025-09-01 17:24:01.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5188
2025-09-01 17:24:01.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3929
2025-09-01 17:24:01.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5095
2025-09-01 17:24:01.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:24:01.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:24:01.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.617
2025-09-01 17:24:01.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.519
2025-09-01 17:24:01.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-09-01 17:24:01.103 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.509
2025-09-01 17:24:01.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:24:01.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:24:01.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:24:01.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:24:01.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:24:01.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:24:01.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:24:01.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:24:01.104 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:24:01.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:24:02.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:24:03.176 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:24:03.825 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:24:04.462 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:24:05.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:24:05.789 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:24:06.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:24:07.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:24:07.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:24:07.107 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:24:07.108 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:24:07.114 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.87 ms, Average inference time: 7.17 ms

2025-09-01 17:24:07.115 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:24:07.207 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:24:07.295 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch572
2025-09-01 17:24:10.117 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 572/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 7.1, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.6, lr: 1.138e-05, size: 416, ETA: 0:09:29
2025-09-01 17:24:13.229 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 572/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.3, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.7, lr: 1.126e-05, size: 288, ETA: 0:09:26
2025-09-01 17:24:16.544 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 572/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.163s, data_time: 0.003s, total_loss: 6.4, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.6, lr: 1.114e-05, size: 576, ETA: 0:09:23
2025-09-01 17:24:19.661 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 572/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 8.4, iou_loss: 2.9, l1_loss: 1.5, conf_loss: 3.4, cls_loss: 0.7, lr: 1.102e-05, size: 480, ETA: 0:09:20
2025-09-01 17:24:22.755 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 572/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.6, lr: 1.090e-05, size: 576, ETA: 0:09:16
2025-09-01 17:24:25.850 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 572/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 1.078e-05, size: 448, ETA: 0:09:13
2025-09-01 17:24:27.200 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:24:33.409 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:24:34.217 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:24:34.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6234
2025-09-01 17:24:34.911 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5232
2025-09-01 17:24:34.946 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4082
2025-09-01 17:24:34.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5183
2025-09-01 17:24:34.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:24:34.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:24:34.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.623
2025-09-01 17:24:34.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.523
2025-09-01 17:24:34.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.408
2025-09-01 17:24:34.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.518
2025-09-01 17:24:34.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:24:34.947 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:24:34.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:24:34.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:24:34.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:24:34.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:24:34.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:24:34.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:24:34.948 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:24:35.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:24:36.485 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:24:37.178 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:24:37.884 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:24:38.606 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:24:39.280 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:24:39.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:24:40.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:24:41.354 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:24:41.354 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:24:41.354 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:24:41.354 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:24:41.361 | 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

2025-09-01 17:24:41.362 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:24:41.447 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:24:41.529 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch573
2025-09-01 17:24:44.410 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 573/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.8, l1_loss: 0.9, conf_loss: 2.5, cls_loss: 0.7, lr: 1.061e-05, size: 512, ETA: 0:09:09
2025-09-01 17:24:47.513 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 573/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, 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: 1.049e-05, size: 384, ETA: 0:09:06
2025-09-01 17:24:50.589 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 573/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.9, iou_loss: 3.1, l1_loss: 1.0, conf_loss: 3.1, cls_loss: 0.7, lr: 1.037e-05, size: 256, ETA: 0:09:03
2025-09-01 17:24:53.642 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 573/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 1.026e-05, size: 544, ETA: 0:09:00
2025-09-01 17:24:56.669 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 573/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 0.9, cls_loss: 0.6, lr: 1.014e-05, size: 416, ETA: 0:08:57
2025-09-01 17:24:59.635 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 573/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.7, lr: 1.003e-05, size: 480, ETA: 0:08:54
2025-09-01 17:25:00.958 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:25:07.303 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:25:08.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:25:08.736 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6177
2025-09-01 17:25:08.869 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5055
2025-09-01 17:25:08.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4026
2025-09-01 17:25:08.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5086
2025-09-01 17:25:08.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:25:08.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:25:08.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.618
2025-09-01 17:25:08.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.506
2025-09-01 17:25:08.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-09-01 17:25:08.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.509
2025-09-01 17:25:08.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:25:08.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:25:08.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:25:08.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:25:08.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:25:08.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:25:08.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:25:08.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:25:08.904 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:25:09.683 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:25:10.416 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:25:11.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:25:11.920 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:25:12.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:25:13.422 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:25:14.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:25:14.910 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:25:15.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:25:15.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:25:15.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:25:15.640 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:25:15.647 | 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

2025-09-01 17:25:15.654 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:25:15.773 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:25:15.884 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch574
2025-09-01 17:25:18.856 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 574/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, 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: 9.862e-06, size: 320, ETA: 0:08:49
2025-09-01 17:25:22.002 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 574/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.5, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 9.749e-06, size: 576, ETA: 0:08:46
2025-09-01 17:25:25.180 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 574/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 1.0, lr: 9.636e-06, size: 480, ETA: 0:08:43
2025-09-01 17:25:28.245 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 574/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 7.5, iou_loss: 2.8, l1_loss: 1.4, conf_loss: 2.5, cls_loss: 0.7, lr: 9.524e-06, size: 576, ETA: 0:08:40
2025-09-01 17:25:31.309 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 574/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, 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: 9.413e-06, size: 480, ETA: 0:08:37
2025-09-01 17:25:34.356 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 574/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.9, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.5, cls_loss: 0.6, lr: 9.302e-06, size: 352, ETA: 0:08:34
2025-09-01 17:25:35.873 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:25:42.023 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:25:42.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:25:43.156 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.5928
2025-09-01 17:25:43.277 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5309
2025-09-01 17:25:43.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3693
2025-09-01 17:25:43.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.4977
2025-09-01 17:25:43.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:25:43.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:25:43.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.593
2025-09-01 17:25:43.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-09-01 17:25:43.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.369
2025-09-01 17:25:43.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.498
2025-09-01 17:25:43.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:25:43.306 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:25:43.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:25:43.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:25:43.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:25:43.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:25:43.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:25:43.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:25:43.307 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:25:43.928 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:25:44.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:25:45.062 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:25:45.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:25:46.263 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:25:46.842 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:25:47.423 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:25:48.004 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:25:48.633 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:25:48.633 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:25:48.633 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 17:25:48.633 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:25:48.640 | 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

2025-09-01 17:25:48.645 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:25:48.733 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:25:48.814 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch575
2025-09-01 17:25:51.655 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 575/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.6, lr: 9.142e-06, size: 512, ETA: 0:08:29
2025-09-01 17:25:54.693 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 575/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, 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: 9.033e-06, size: 288, ETA: 0:08:26
2025-09-01 17:25:58.003 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 575/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.163s, data_time: 0.001s, total_loss: 23.0, iou_loss: 4.3, l1_loss: 3.2, conf_loss: 13.9, cls_loss: 1.6, lr: 8.925e-06, size: 448, ETA: 0:08:23
2025-09-01 17:26:01.026 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 575/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 9.3, iou_loss: 2.6, l1_loss: 1.3, conf_loss: 4.4, cls_loss: 1.0, lr: 8.817e-06, size: 544, ETA: 0:08:20
2025-09-01 17:26:04.169 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 575/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 8.709e-06, size: 288, ETA: 0:08:17
2025-09-01 17:26:07.213 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 575/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, 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: 8.603e-06, size: 416, ETA: 0:08:14
2025-09-01 17:26:08.572 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:26:15.027 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:26:15.872 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:26:16.473 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6174
2025-09-01 17:26:16.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5409
2025-09-01 17:26:16.644 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4132
2025-09-01 17:26:16.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5238
2025-09-01 17:26:16.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:26:16.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:26:16.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.617
2025-09-01 17:26:16.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-09-01 17:26:16.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-09-01 17:26:16.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.524
2025-09-01 17:26:16.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:26:16.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:26:16.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:26:16.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:26:16.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:26:16.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:26:16.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:26:16.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:26:16.646 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:26:17.394 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:26:18.148 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:26:18.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:26:19.622 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:26:20.404 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:26:21.128 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:26:21.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:26:22.605 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:26:23.376 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:26:23.376 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:26:23.376 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:26:23.376 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:26:23.392 | 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

2025-09-01 17:26:23.393 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:26:23.514 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:26:23.601 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch576
2025-09-01 17:26:26.608 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 576/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.2, iou_loss: 1.8, l1_loss: 0.9, conf_loss: 1.9, cls_loss: 0.5, lr: 8.450e-06, size: 512, ETA: 0:08:10
2025-09-01 17:26:29.725 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 576/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 2.8, cls_loss: 0.7, lr: 8.345e-06, size: 320, ETA: 0:08:07
2025-09-01 17:26:32.698 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 576/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.5, l1_loss: 0.6, conf_loss: 1.5, cls_loss: 0.4, lr: 8.240e-06, size: 320, ETA: 0:08:04
2025-09-01 17:26:35.817 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 576/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.3, cls_loss: 0.6, lr: 8.137e-06, size: 512, ETA: 0:08:01
2025-09-01 17:26:38.950 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 576/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.001s, total_loss: 6.3, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 2.8, cls_loss: 0.8, lr: 8.034e-06, size: 352, ETA: 0:07:57
2025-09-01 17:26:41.822 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 576/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, 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: 7.931e-06, size: 352, ETA: 0:07:54
2025-09-01 17:26:43.162 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:26:49.369 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:26:50.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:26:51.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6204
2025-09-01 17:26:51.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5406
2025-09-01 17:26:51.245 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4070
2025-09-01 17:26:51.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5227
2025-09-01 17:26:51.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:26:51.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:26:51.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.620
2025-09-01 17:26:51.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-09-01 17:26:51.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.407
2025-09-01 17:26:51.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.523
2025-09-01 17:26:51.246 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:26:51.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:26:51.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:26:51.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:26:51.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:26:51.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:26:51.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:26:51.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:26:51.247 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:26:52.131 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:26:52.969 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:26:53.873 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:26:54.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:26:55.571 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:26:56.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:26:57.254 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:26:58.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:26:58.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:26:58.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:26:58.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:26:58.958 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:26:58.965 | 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: 7.02 ms

2025-09-01 17:26:58.966 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:26:59.046 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:26:59.135 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch577
2025-09-01 17:27:02.011 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 577/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.7, iou_loss: 2.5, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.6, lr: 7.784e-06, size: 544, ETA: 0:07:50
2025-09-01 17:27:05.080 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 577/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.003s, total_loss: 5.3, iou_loss: 1.8, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.6, lr: 7.683e-06, size: 480, ETA: 0:07:47
2025-09-01 17:27:08.128 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 577/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, 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: 0.7, lr: 7.583e-06, size: 448, ETA: 0:07:44
2025-09-01 17:27:11.307 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 577/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.9, cls_loss: 0.9, lr: 7.484e-06, size: 256, ETA: 0:07:41
2025-09-01 17:27:14.316 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 577/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.8, iou_loss: 2.4, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 7.385e-06, size: 320, ETA: 0:07:38
2025-09-01 17:27:17.324 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 577/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.7, iou_loss: 2.7, l1_loss: 1.1, conf_loss: 2.3, cls_loss: 0.7, lr: 7.287e-06, size: 384, ETA: 0:07:35
2025-09-01 17:27:18.660 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:27:24.989 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:27:25.697 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:27:26.185 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6200
2025-09-01 17:27:26.271 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5254
2025-09-01 17:27:26.334 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4237
2025-09-01 17:27:26.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5230
2025-09-01 17:27:26.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:27:26.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:27:26.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.620
2025-09-01 17:27:26.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.525
2025-09-01 17:27:26.336 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.424
2025-09-01 17:27:26.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.523
2025-09-01 17:27:26.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:27:26.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:27:26.337 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:27:26.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:27:26.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:27:26.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:27:26.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:27:26.338 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:27:26.339 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:27:27.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:27:27.621 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:27:28.207 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:27:28.799 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:27:29.497 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:27:30.140 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:27:30.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:27:31.490 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:27:32.135 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:27:32.135 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 17:27:32.136 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:27:32.136 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:27:32.143 | 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: 6.99 ms

2025-09-01 17:27:32.149 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:27:32.237 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:27:32.361 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch578
2025-09-01 17:27:35.328 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 578/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 7.1, iou_loss: 3.1, l1_loss: 0.9, conf_loss: 2.4, cls_loss: 0.7, lr: 7.145e-06, size: 256, ETA: 0:07:30
2025-09-01 17:27:38.311 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 578/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 2.1, cls_loss: 0.5, lr: 7.049e-06, size: 512, ETA: 0:07:27
2025-09-01 17:27:41.473 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 578/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, 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: 6.953e-06, size: 544, ETA: 0:07:24
2025-09-01 17:27:44.593 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 578/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.153s, data_time: 0.001s, total_loss: 3.7, iou_loss: 2.0, l1_loss: 0.5, conf_loss: 0.7, cls_loss: 0.6, lr: 6.858e-06, size: 320, ETA: 0:07:21
2025-09-01 17:27:47.753 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 578/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.155s, 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: 6.763e-06, size: 320, ETA: 0:07:18
2025-09-01 17:27:50.787 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 578/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.8, lr: 6.669e-06, size: 448, ETA: 0:07:15
2025-09-01 17:27:52.134 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:27:58.112 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:27:58.824 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:27:59.221 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6152
2025-09-01 17:27:59.314 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5314
2025-09-01 17:27:59.396 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3977
2025-09-01 17:27:59.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5148
2025-09-01 17:27:59.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:27:59.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:27:59.397 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.615
2025-09-01 17:27:59.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.531
2025-09-01 17:27:59.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.398
2025-09-01 17:27:59.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.515
2025-09-01 17:27:59.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:27:59.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:27:59.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:27:59.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:27:59.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:27:59.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:27:59.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:27:59.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:27:59.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:28:00.047 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:28:00.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:28:01.144 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:28:01.727 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:28:02.340 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:28:02.889 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:28:03.449 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:28:03.996 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:28:04.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:28:04.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:28:04.590 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:28:04.591 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:28:04.597 | 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

2025-09-01 17:28:04.598 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:28:04.687 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:28:04.767 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch579
2025-09-01 17:28:07.673 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 579/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.6, lr: 6.534e-06, size: 352, ETA: 0:07:10
2025-09-01 17:28:10.764 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 579/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 5.3, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.8, cls_loss: 0.7, lr: 6.442e-06, size: 320, ETA: 0:07:07
2025-09-01 17:28:13.918 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 579/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 8.8, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 4.2, cls_loss: 0.7, lr: 6.350e-06, size: 256, ETA: 0:07:04
2025-09-01 17:28:17.154 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 579/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.159s, 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: 6.259e-06, size: 416, ETA: 0:07:01
2025-09-01 17:28:20.104 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 579/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 4.7, iou_loss: 2.0, l1_loss: 0.7, conf_loss: 1.4, cls_loss: 0.5, lr: 6.169e-06, size: 544, ETA: 0:06:58
2025-09-01 17:28:23.285 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 579/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 6.079e-06, size: 288, ETA: 0:06:55
2025-09-01 17:28:24.562 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:28:30.775 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:28:32.152 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:28:33.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6022
2025-09-01 17:28:33.235 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5279
2025-09-01 17:28:33.272 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4098
2025-09-01 17:28:33.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5133
2025-09-01 17:28:33.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:28:33.273 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:28:33.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.602
2025-09-01 17:28:33.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.528
2025-09-01 17:28:33.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.410
2025-09-01 17:28:33.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-09-01 17:28:33.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:28:33.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:28:33.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:28:33.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:28:33.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:28:33.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:28:33.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:28:33.274 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:28:33.275 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:28:34.476 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:28:35.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:28:36.860 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:28:38.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:28:39.234 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:28:40.398 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:28:41.588 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:28:42.731 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:28:43.868 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:28:43.869 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:28:43.869 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:28:43.869 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:28:43.876 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.22 ms, Average NMS time: 0.89 ms, Average inference time: 7.11 ms

2025-09-01 17:28:43.878 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:28:43.959 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:28:44.052 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch580
2025-09-01 17:28:46.883 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 580/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.140s, data_time: 0.001s, total_loss: 7.2, iou_loss: 2.9, l1_loss: 1.0, conf_loss: 2.6, cls_loss: 0.7, lr: 5.950e-06, size: 320, ETA: 0:06:51
2025-09-01 17:28:49.834 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 580/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.0, iou_loss: 1.6, l1_loss: 0.6, conf_loss: 2.3, cls_loss: 0.6, lr: 5.862e-06, size: 352, ETA: 0:06:48
2025-09-01 17:28:52.797 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 580/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.146s, 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: 5.775e-06, size: 384, ETA: 0:06:45
2025-09-01 17:28:55.860 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 580/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 4.0, iou_loss: 1.6, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.5, lr: 5.688e-06, size: 576, ETA: 0:06:42
2025-09-01 17:28:59.106 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 580/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.4, l1_loss: 0.6, conf_loss: 1.9, cls_loss: 0.7, lr: 5.602e-06, size: 352, ETA: 0:06:38
2025-09-01 17:29:01.995 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 580/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.143s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.8, l1_loss: 0.8, conf_loss: 1.1, cls_loss: 0.6, lr: 5.516e-06, size: 544, ETA: 0:06:35
2025-09-01 17:29:03.384 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:29:09.612 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:29:10.412 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:29:10.883 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6146
2025-09-01 17:29:11.022 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5320
2025-09-01 17:29:11.056 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3927
2025-09-01 17:29:11.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5131
2025-09-01 17:29:11.057 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:29:11.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:29:11.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.615
2025-09-01 17:29:11.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-09-01 17:29:11.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.393
2025-09-01 17:29:11.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-09-01 17:29:11.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:29:11.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:29:11.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:29:11.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:29:11.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:29:11.058 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:29:11.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:29:11.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:29:11.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:29:11.721 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:29:12.411 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:29:13.042 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:29:13.700 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:29:14.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:29:15.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:29:15.656 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:29:16.292 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:29:16.972 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:29:16.973 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:29:16.973 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:29:16.973 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:29:16.980 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.12 ms, Average NMS time: 0.85 ms, Average inference time: 6.97 ms

2025-09-01 17:29:16.981 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:29:17.067 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:29:17.148 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch581
2025-09-01 17:29:20.104 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 581/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 5.394e-06, size: 512, ETA: 0:06:31
2025-09-01 17:29:23.113 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 581/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.6, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.5, lr: 5.310e-06, size: 544, ETA: 0:06:28
2025-09-01 17:29:26.256 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 581/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 5.226e-06, size: 288, ETA: 0:06:25
2025-09-01 17:29:29.255 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 581/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.1, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.1, cls_loss: 0.5, lr: 5.144e-06, size: 352, ETA: 0:06:22
2025-09-01 17:29:32.504 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 581/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.160s, data_time: 0.002s, total_loss: 12.0, iou_loss: 2.7, l1_loss: 1.8, conf_loss: 6.2, cls_loss: 1.2, lr: 5.062e-06, size: 576, ETA: 0:06:19
2025-09-01 17:29:35.684 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 581/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.157s, 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: 4.981e-06, size: 576, ETA: 0:06:16
2025-09-01 17:29:37.013 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:29:43.400 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:29:44.560 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:29:45.332 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6153
2025-09-01 17:29:45.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5292
2025-09-01 17:29:45.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4055
2025-09-01 17:29:45.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5167
2025-09-01 17:29:45.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:29:45.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:29:45.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.615
2025-09-01 17:29:45.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-09-01 17:29:45.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-09-01 17:29:45.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.517
2025-09-01 17:29:45.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:29:45.546 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:29:45.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:29:45.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:29:45.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:29:45.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:29:45.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:29:45.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:29:45.547 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:29:46.555 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:29:47.575 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:29:48.610 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:29:49.670 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:29:50.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:29:51.738 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:29:52.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:29:53.776 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:29:54.799 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:29:54.799 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:29:54.799 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:29:54.799 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:29:54.806 | 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: 7.25 ms

2025-09-01 17:29:54.810 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:29:54.911 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:29:54.991 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch582
2025-09-01 17:29:57.838 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 582/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.141s, 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: 4.864e-06, size: 544, ETA: 0:06:11
2025-09-01 17:30:01.059 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 582/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.158s, data_time: 0.002s, total_loss: 4.1, iou_loss: 1.7, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.6, lr: 4.784e-06, size: 576, ETA: 0:06:08
2025-09-01 17:30:04.323 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 582/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.162s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 0.9, cls_loss: 0.6, lr: 4.706e-06, size: 352, ETA: 0:06:05
2025-09-01 17:30:07.305 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 582/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 8.5, iou_loss: 2.7, l1_loss: 1.2, conf_loss: 3.7, cls_loss: 0.9, lr: 4.627e-06, size: 320, ETA: 0:06:02
2025-09-01 17:30:10.387 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 582/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.2, l1_loss: 1.1, conf_loss: 0.9, cls_loss: 0.6, lr: 4.549e-06, size: 576, ETA: 0:05:59
2025-09-01 17:30:13.551 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 582/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.7, lr: 4.472e-06, size: 512, ETA: 0:05:56
2025-09-01 17:30:14.923 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:30:21.061 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:30:21.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:30:22.164 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6141
2025-09-01 17:30:22.328 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5348
2025-09-01 17:30:22.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3833
2025-09-01 17:30:22.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5107
2025-09-01 17:30:22.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:30:22.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:30:22.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.614
2025-09-01 17:30:22.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-09-01 17:30:22.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.383
2025-09-01 17:30:22.359 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.511
2025-09-01 17:30:22.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:30:22.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:30:22.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:30:22.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:30:22.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:30:22.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:30:22.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:30:22.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:30:22.360 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:30:22.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:30:23.424 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:30:23.985 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:30:24.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:30:25.099 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:30:25.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:30:26.160 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:30:26.695 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:30:27.262 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:30:27.263 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:30:27.263 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:30:27.263 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:30:27.269 | 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: 7.14 ms

2025-09-01 17:30:27.270 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:30:27.366 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:30:27.454 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch583
2025-09-01 17:30:30.346 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 583/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 1.7, cls_loss: 0.7, lr: 4.362e-06, size: 384, ETA: 0:05:52
2025-09-01 17:30:33.411 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 583/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, 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: 4.287e-06, size: 512, ETA: 0:05:48
2025-09-01 17:30:36.429 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 583/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, 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: 4.212e-06, size: 352, ETA: 0:05:45
2025-09-01 17:30:39.504 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 583/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.3, l1_loss: 1.0, conf_loss: 2.1, cls_loss: 0.7, lr: 4.138e-06, size: 480, ETA: 0:05:42
2025-09-01 17:30:42.592 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 583/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, 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: 4.064e-06, size: 320, ETA: 0:05:39
2025-09-01 17:30:45.643 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 583/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 4.7, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.2, cls_loss: 0.6, lr: 3.991e-06, size: 256, ETA: 0:05:36
2025-09-01 17:30:46.951 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:30:53.190 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:30:54.134 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:30:54.854 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6101
2025-09-01 17:30:54.972 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5114
2025-09-01 17:30:55.008 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3945
2025-09-01 17:30:55.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5053
2025-09-01 17:30:55.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:30:55.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:30:55.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.610
2025-09-01 17:30:55.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.511
2025-09-01 17:30:55.009 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.394
2025-09-01 17:30:55.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.505
2025-09-01 17:30:55.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:30:55.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:30:55.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:30:55.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:30:55.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:30:55.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:30:55.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:30:55.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:30:55.010 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:30:55.852 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:30:56.711 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:30:57.587 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:30:58.399 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:30:59.257 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:31:00.059 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:31:00.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:31:01.748 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:31:02.613 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:31:02.613 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:31:02.613 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:31:02.613 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:31:02.620 | 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

2025-09-01 17:31:02.621 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:31:02.705 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:31:02.787 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch584
2025-09-01 17:31:05.691 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 584/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 1.1, lr: 3.887e-06, size: 320, ETA: 0:05:32
2025-09-01 17:31:08.684 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 584/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, 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: 3.816e-06, size: 480, ETA: 0:05:29
2025-09-01 17:31:11.713 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 584/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, 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: 3.745e-06, size: 384, ETA: 0:05:26
2025-09-01 17:31:14.679 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 584/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.4, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 2.1, cls_loss: 0.7, lr: 3.676e-06, size: 512, ETA: 0:05:23
2025-09-01 17:31:17.669 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 584/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 7.7, iou_loss: 2.7, l1_loss: 1.3, conf_loss: 3.1, cls_loss: 0.6, lr: 3.606e-06, size: 544, ETA: 0:05:20
2025-09-01 17:31:20.722 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 584/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.4, cls_loss: 0.9, lr: 3.538e-06, size: 256, ETA: 0:05:16
2025-09-01 17:31:22.048 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:31:28.182 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:31:29.224 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:31:29.835 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6122
2025-09-01 17:31:30.011 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5412
2025-09-01 17:31:30.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4026
2025-09-01 17:31:30.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5187
2025-09-01 17:31:30.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:31:30.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:31:30.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.612
2025-09-01 17:31:30.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-09-01 17:31:30.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.403
2025-09-01 17:31:30.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.519
2025-09-01 17:31:30.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:31:30.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:31:30.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:31:30.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:31:30.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:31:30.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:31:30.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:31:30.052 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:31:30.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:31:31.023 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:31:31.829 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:31:32.672 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:31:33.501 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:31:34.356 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:31:35.173 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:31:36.041 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:31:36.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:31:37.774 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:31:37.774 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:31:37.774 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:31:37.775 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:31:37.782 | 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

2025-09-01 17:31:37.783 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:31:37.864 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:31:37.946 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch585
2025-09-01 17:31:40.826 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 585/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.142s, data_time: 0.001s, total_loss: 8.7, iou_loss: 2.8, l1_loss: 1.0, conf_loss: 4.1, cls_loss: 0.8, lr: 3.440e-06, size: 480, ETA: 0:05:12
2025-09-01 17:31:43.881 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 585/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 2.6, cls_loss: 0.7, lr: 3.373e-06, size: 512, ETA: 0:05:09
2025-09-01 17:31:46.822 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 585/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.7, l1_loss: 0.8, conf_loss: 2.0, cls_loss: 0.6, lr: 3.306e-06, size: 256, ETA: 0:05:06
2025-09-01 17:31:49.852 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 585/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 8.8, iou_loss: 3.4, l1_loss: 1.1, conf_loss: 3.5, cls_loss: 0.8, lr: 3.241e-06, size: 384, ETA: 0:05:03
2025-09-01 17:31:52.995 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 585/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, 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: 3.176e-06, size: 512, ETA: 0:05:00
2025-09-01 17:31:56.047 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 585/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, 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: 3.111e-06, size: 416, ETA: 0:04:57
2025-09-01 17:31:57.429 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:32:03.606 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:32:04.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:32:04.934 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6101
2025-09-01 17:32:05.035 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5414
2025-09-01 17:32:05.069 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4025
2025-09-01 17:32:05.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5180
2025-09-01 17:32:05.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:32:05.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:32:05.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.610
2025-09-01 17:32:05.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-09-01 17:32:05.070 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-09-01 17:32:05.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.518
2025-09-01 17:32:05.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:32:05.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:32:05.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:32:05.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:32:05.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:32:05.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:32:05.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:32:05.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:32:05.071 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:32:05.760 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:32:06.401 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:32:07.055 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:32:07.751 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:32:08.400 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:32:09.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:32:09.706 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:32:10.402 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:32:11.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:32:11.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:32:11.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:32:11.029 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:32:11.036 | 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: 7.12 ms

2025-09-01 17:32:11.037 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:32:11.122 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:32:11.203 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch586
2025-09-01 17:32:14.171 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 586/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.8, l1_loss: 0.8, conf_loss: 1.8, cls_loss: 0.8, lr: 3.019e-06, size: 288, ETA: 0:04:52
2025-09-01 17:32:17.213 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 586/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.6, lr: 2.957e-06, size: 448, ETA: 0:04:49
2025-09-01 17:32:20.240 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 586/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.7, lr: 2.895e-06, size: 512, ETA: 0:04:46
2025-09-01 17:32:23.266 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 586/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.8, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.5, cls_loss: 0.6, lr: 2.833e-06, size: 480, ETA: 0:04:43
2025-09-01 17:32:26.248 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 586/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 3.1, iou_loss: 1.4, l1_loss: 0.4, conf_loss: 0.9, cls_loss: 0.5, lr: 2.772e-06, size: 320, ETA: 0:04:40
2025-09-01 17:32:29.388 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 586/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 2.712e-06, size: 480, ETA: 0:04:37
2025-09-01 17:32:30.753 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:32:37.330 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:32:38.051 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:32:38.559 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6201
2025-09-01 17:32:38.658 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5357
2025-09-01 17:32:38.689 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4018
2025-09-01 17:32:38.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5192
2025-09-01 17:32:38.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:32:38.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:32:38.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.620
2025-09-01 17:32:38.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-09-01 17:32:38.690 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.402
2025-09-01 17:32:38.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.519
2025-09-01 17:32:38.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:32:38.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:32:38.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:32:38.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:32:38.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:32:38.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:32:38.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:32:38.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:32:38.691 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:32:39.362 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:32:39.977 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:32:40.590 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:32:41.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:32:41.866 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:32:42.481 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:32:43.091 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:32:43.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:32:44.354 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:32:44.354 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:32:44.355 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:32:44.355 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:32:44.362 | 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: 7.16 ms

2025-09-01 17:32:44.363 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:32:44.455 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:32:44.540 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch587
2025-09-01 17:32:47.428 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 587/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 6.5, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.5, cls_loss: 0.8, lr: 2.626e-06, size: 320, ETA: 0:04:33
2025-09-01 17:32:50.468 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 587/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.149s, 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: 2.568e-06, size: 544, ETA: 0:04:30
2025-09-01 17:32:53.660 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 587/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.6, cls_loss: 0.6, lr: 2.510e-06, size: 352, ETA: 0:04:26
2025-09-01 17:32:56.840 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 587/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.157s, 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: 2.453e-06, size: 480, ETA: 0:04:23
2025-09-01 17:32:59.879 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 587/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.1, l1_loss: 0.6, conf_loss: 1.4, cls_loss: 0.7, lr: 2.396e-06, size: 256, ETA: 0:04:20
2025-09-01 17:33:02.885 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 587/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 7.8, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 3.0, cls_loss: 0.9, lr: 2.341e-06, size: 256, ETA: 0:04:17
2025-09-01 17:33:04.175 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:33:10.655 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:33:11.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:33:12.553 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6159
2025-09-01 17:33:12.744 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5295
2025-09-01 17:33:12.778 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4064
2025-09-01 17:33:12.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5173
2025-09-01 17:33:12.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:33:12.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:33:12.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.616
2025-09-01 17:33:12.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.529
2025-09-01 17:33:12.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-09-01 17:33:12.779 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.517
2025-09-01 17:33:12.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:33:12.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:33:12.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:33:12.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:33:12.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:33:12.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:33:12.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:33:12.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:33:12.780 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:33:13.802 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:33:14.815 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:33:15.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:33:16.819 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:33:17.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:33:18.823 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:33:19.907 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:33:20.898 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:33:21.913 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:33:21.913 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:33:21.914 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:33:21.914 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:33:21.927 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.37 ms, Average NMS time: 0.93 ms, Average inference time: 7.29 ms

2025-09-01 17:33:21.928 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:33:22.045 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:33:22.149 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch588
2025-09-01 17:33:24.964 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 588/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.139s, data_time: 0.001s, total_loss: 6.9, iou_loss: 2.6, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 2.261e-06, size: 512, ETA: 0:04:13
2025-09-01 17:33:28.024 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 588/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 7.8, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 4.3, cls_loss: 0.5, lr: 2.207e-06, size: 576, ETA: 0:04:10
2025-09-01 17:33:31.067 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 588/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 3.1, iou_loss: 1.2, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.4, lr: 2.153e-06, size: 352, ETA: 0:04:07
2025-09-01 17:33:34.189 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 588/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.3, l1_loss: 1.4, conf_loss: 2.6, cls_loss: 0.7, lr: 2.100e-06, size: 544, ETA: 0:04:04
2025-09-01 17:33:37.235 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 588/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.150s, 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: 2.048e-06, size: 352, ETA: 0:04:01
2025-09-01 17:33:40.178 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 588/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 3.8, iou_loss: 1.8, l1_loss: 0.6, conf_loss: 0.9, cls_loss: 0.5, lr: 1.996e-06, size: 352, ETA: 0:03:58
2025-09-01 17:33:41.523 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:33:47.737 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:33:48.685 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:33:49.335 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6149
2025-09-01 17:33:49.471 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5424
2025-09-01 17:33:49.505 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4136
2025-09-01 17:33:49.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5236
2025-09-01 17:33:49.506 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:33:49.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:33:49.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.615
2025-09-01 17:33:49.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-09-01 17:33:49.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.414
2025-09-01 17:33:49.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.524
2025-09-01 17:33:49.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:33:49.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:33:49.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:33:49.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:33:49.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:33:49.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:33:49.507 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:33:49.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:33:49.508 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:33:50.364 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:33:51.184 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:33:52.033 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:33:52.839 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:33:53.680 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:33:54.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:33:55.319 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:33:56.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:33:56.945 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:33:56.946 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.25
2025-09-01 17:33:56.946 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:33:56.946 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:33:56.953 | 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

2025-09-01 17:33:56.954 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:33:57.036 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:33:57.119 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch589
2025-09-01 17:33:59.908 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 589/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.137s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.6, cls_loss: 0.5, lr: 1.923e-06, size: 352, ETA: 0:03:53
2025-09-01 17:34:02.900 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 589/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 2.2, cls_loss: 0.7, lr: 1.873e-06, size: 448, ETA: 0:03:50
2025-09-01 17:34:05.928 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 589/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.150s, 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: 1.823e-06, size: 416, ETA: 0:03:47
2025-09-01 17:34:08.846 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 589/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.7, lr: 1.775e-06, size: 544, ETA: 0:03:44
2025-09-01 17:34:11.917 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 589/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.8, iou_loss: 1.9, l1_loss: 1.0, conf_loss: 2.2, cls_loss: 0.6, lr: 1.727e-06, size: 480, ETA: 0:03:41
2025-09-01 17:34:14.920 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 589/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 9.2, iou_loss: 2.9, l1_loss: 1.5, conf_loss: 3.9, cls_loss: 0.8, lr: 1.679e-06, size: 544, ETA: 0:03:38
2025-09-01 17:34:16.313 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:34:22.530 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:34:23.210 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:34:23.618 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6097
2025-09-01 17:34:23.814 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5225
2025-09-01 17:34:23.844 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3767
2025-09-01 17:34:23.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5029
2025-09-01 17:34:23.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:34:23.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:34:23.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.610
2025-09-01 17:34:23.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.522
2025-09-01 17:34:23.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.377
2025-09-01 17:34:23.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.503
2025-09-01 17:34:23.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:34:23.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:34:23.845 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:34:23.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:34:23.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:34:23.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:34:23.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:34:23.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:34:23.846 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:34:24.429 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:34:25.043 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:34:25.626 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:34:26.214 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:34:26.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:34:27.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:34:27.955 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:34:28.515 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:34:29.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:34:29.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.26
2025-09-01 17:34:29.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.50
2025-09-01 17:34:29.071 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:34:29.078 | 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: 7.17 ms

2025-09-01 17:34:29.079 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:34:29.204 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:34:29.278 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch590
2025-09-01 17:34:32.288 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 590/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 6.4, iou_loss: 2.6, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.6, lr: 1.612e-06, size: 352, ETA: 0:03:33
2025-09-01 17:34:35.324 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 590/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.9, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.2, cls_loss: 0.6, lr: 1.566e-06, size: 512, ETA: 0:03:30
2025-09-01 17:34:38.287 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 590/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 1.4, cls_loss: 0.6, lr: 1.521e-06, size: 320, ETA: 0:03:27
2025-09-01 17:34:41.195 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 590/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.143s, data_time: 0.001s, total_loss: 6.7, iou_loss: 2.9, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 1.477e-06, size: 352, ETA: 0:03:24
2025-09-01 17:34:44.302 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 590/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.4, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.0, cls_loss: 0.6, lr: 1.433e-06, size: 416, ETA: 0:03:21
2025-09-01 17:34:47.328 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 590/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 4.7, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.6, cls_loss: 0.6, lr: 1.390e-06, size: 256, ETA: 0:03:18
2025-09-01 17:34:48.663 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:34:54.742 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:34:55.798 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:34:56.531 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6209
2025-09-01 17:34:56.663 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5325
2025-09-01 17:34:56.739 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4115
2025-09-01 17:34:56.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5216
2025-09-01 17:34:56.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:34:56.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:34:56.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.621
2025-09-01 17:34:56.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-09-01 17:34:56.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-09-01 17:34:56.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.522
2025-09-01 17:34:56.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:34:56.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:34:56.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:34:56.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:34:56.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:34:56.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:34:56.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:34:56.741 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:34:56.742 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:34:57.645 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:34:58.550 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:34:59.545 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:35:00.431 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:35:01.367 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:35:02.293 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:35:03.187 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:35:04.117 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:35:05.004 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:35:05.005 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:35:05.005 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:35:05.005 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:35:05.012 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.90 ms, Average inference time: 7.19 ms

2025-09-01 17:35:05.013 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:35:05.149 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:35:05.224 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch591
2025-09-01 17:35:08.118 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 591/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.9, l1_loss: 0.8, conf_loss: 3.0, cls_loss: 0.9, lr: 1.328e-06, size: 256, ETA: 0:03:14
2025-09-01 17:35:11.230 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 591/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.154s, data_time: 0.003s, total_loss: 4.9, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.6, lr: 1.287e-06, size: 576, ETA: 0:03:11
2025-09-01 17:35:14.393 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 591/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.9, l1_loss: 0.8, conf_loss: 1.2, cls_loss: 0.5, lr: 1.246e-06, size: 576, ETA: 0:03:08
2025-09-01 17:35:17.595 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 591/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.3, l1_loss: 1.2, conf_loss: 2.0, cls_loss: 0.6, lr: 1.206e-06, size: 576, ETA: 0:03:04
2025-09-01 17:35:20.656 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 591/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 3.5, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.5, lr: 1.166e-06, size: 320, ETA: 0:03:01
2025-09-01 17:35:23.649 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 591/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.147s, 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: 1.127e-06, size: 288, ETA: 0:02:58
2025-09-01 17:35:25.070 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:35:31.219 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:35:32.086 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:35:32.633 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6177
2025-09-01 17:35:32.712 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5345
2025-09-01 17:35:32.791 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.3880
2025-09-01 17:35:32.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5134
2025-09-01 17:35:32.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:35:32.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:35:32.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.618
2025-09-01 17:35:32.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.534
2025-09-01 17:35:32.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.388
2025-09-01 17:35:32.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.513
2025-09-01 17:35:32.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:35:32.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:35:32.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:35:32.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:35:32.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:35:32.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:35:32.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:35:32.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:35:32.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:35:33.451 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:35:34.084 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:35:34.710 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:35:35.387 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:35:36.020 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:35:36.650 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:35:37.329 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:35:37.966 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:35:38.597 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.51
2025-09-01 17:35:38.598 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:35:38.604 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.29 ms, Average NMS time: 0.90 ms, Average inference time: 7.19 ms

2025-09-01 17:35:38.605 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:35:38.694 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:35:38.776 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch592
2025-09-01 17:35:41.727 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 592/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 3.7, iou_loss: 1.8, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.5, lr: 1.072e-06, size: 384, ETA: 0:02:54
2025-09-01 17:35:44.758 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 592/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.150s, data_time: 0.006s, total_loss: 5.2, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.5, cls_loss: 0.8, lr: 1.035e-06, size: 448, ETA: 0:02:51
2025-09-01 17:35:47.799 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 592/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 6.2, iou_loss: 2.5, l1_loss: 0.8, conf_loss: 2.1, cls_loss: 0.7, lr: 9.984e-07, size: 288, ETA: 0:02:48
2025-09-01 17:35:50.984 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 592/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.157s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.7, lr: 9.624e-07, size: 288, ETA: 0:02:45
2025-09-01 17:35:54.015 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 592/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.149s, 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: 9.272e-07, size: 544, ETA: 0:02:42
2025-09-01 17:35:57.027 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 592/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 6.8, iou_loss: 2.4, l1_loss: 1.1, conf_loss: 2.6, cls_loss: 0.7, lr: 8.925e-07, size: 416, ETA: 0:02:39
2025-09-01 17:35:58.333 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:36:04.500 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:36:05.267 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:36:05.777 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6204
2025-09-01 17:36:05.867 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5349
2025-09-01 17:36:05.901 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4062
2025-09-01 17:36:05.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5205
2025-09-01 17:36:05.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:36:05.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:36:05.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.620
2025-09-01 17:36:05.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.535
2025-09-01 17:36:05.902 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.406
2025-09-01 17:36:05.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.521
2025-09-01 17:36:05.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:36:05.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:36:05.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:36:05.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:36:05.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:36:05.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:36:05.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:36:05.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:36:05.903 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:36:06.569 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:36:07.196 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:36:07.820 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:36:08.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:36:09.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:36:09.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:36:10.393 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:36:11.031 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:36:11.661 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:36:11.661 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:36:11.661 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:36:11.661 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:36:11.668 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.26 ms, Average NMS time: 0.87 ms, Average inference time: 7.13 ms

2025-09-01 17:36:11.673 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:36:11.758 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:36:11.838 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch593
2025-09-01 17:36:14.690 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 593/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.141s, data_time: 0.002s, total_loss: 4.6, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.6, lr: 8.435e-07, size: 320, ETA: 0:02:34
2025-09-01 17:36:17.670 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 593/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.146s, data_time: 0.003s, total_loss: 3.9, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 1.3, cls_loss: 0.5, lr: 8.105e-07, size: 288, ETA: 0:02:31
2025-09-01 17:36:20.612 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 593/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 0.9, conf_loss: 1.2, cls_loss: 0.9, lr: 7.782e-07, size: 512, ETA: 0:02:28
2025-09-01 17:36:23.681 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 593/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.1, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 7.465e-07, size: 544, ETA: 0:02:25
2025-09-01 17:36:26.648 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 593/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.4, iou_loss: 1.8, l1_loss: 0.7, conf_loss: 1.3, cls_loss: 0.5, lr: 7.154e-07, size: 448, ETA: 0:02:22
2025-09-01 17:36:29.662 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 593/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.150s, data_time: 0.002s, total_loss: 5.4, iou_loss: 1.9, l1_loss: 1.1, conf_loss: 1.8, cls_loss: 0.5, lr: 6.851e-07, size: 576, ETA: 0:02:19
2025-09-01 17:36:31.012 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:36:37.257 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:36:38.212 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:36:38.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6213
2025-09-01 17:36:39.026 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5417
2025-09-01 17:36:39.111 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4077
2025-09-01 17:36:39.112 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5236
2025-09-01 17:36:39.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:36:39.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:36:39.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.621
2025-09-01 17:36:39.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.542
2025-09-01 17:36:39.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.408
2025-09-01 17:36:39.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.524
2025-09-01 17:36:39.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:36:39.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:36:39.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:36:39.113 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:36:39.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:36:39.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:36:39.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:36:39.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:36:39.114 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:36:40.028 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:36:40.876 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:36:41.686 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:36:42.516 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:36:43.322 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:36:44.165 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:36:44.981 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:36:45.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:36:46.638 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:36:46.638 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.28
2025-09-01 17:36:46.638 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:36:46.638 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:36:46.645 | 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

2025-09-01 17:36:46.646 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:36:46.727 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:36:46.814 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch594
2025-09-01 17:36:49.732 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 594/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.144s, data_time: 0.002s, total_loss: 6.1, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.9, cls_loss: 0.7, lr: 6.422e-07, size: 512, ETA: 0:02:14
2025-09-01 17:36:52.809 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 594/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.152s, data_time: 0.003s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 1.6, cls_loss: 0.6, lr: 6.134e-07, size: 256, ETA: 0:02:11
2025-09-01 17:36:55.791 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 594/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.3, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.7, lr: 5.853e-07, size: 384, ETA: 0:02:08
2025-09-01 17:36:58.814 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 594/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.5, iou_loss: 2.4, l1_loss: 0.9, conf_loss: 1.5, cls_loss: 0.6, lr: 5.579e-07, size: 352, ETA: 0:02:05
2025-09-01 17:37:01.802 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 594/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.9, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.0, cls_loss: 0.7, lr: 5.311e-07, size: 416, ETA: 0:02:02
2025-09-01 17:37:04.789 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 594/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.148s, data_time: 0.002s, total_loss: 3.6, iou_loss: 1.6, l1_loss: 0.5, conf_loss: 0.9, cls_loss: 0.5, lr: 5.050e-07, size: 416, ETA: 0:01:59
2025-09-01 17:37:06.163 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:37:12.390 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:37:13.305 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:37:13.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6119
2025-09-01 17:37:14.081 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5411
2025-09-01 17:37:14.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4190
2025-09-01 17:37:14.118 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5240
2025-09-01 17:37:14.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:37:14.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:37:14.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.612
2025-09-01 17:37:14.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-09-01 17:37:14.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.419
2025-09-01 17:37:14.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.524
2025-09-01 17:37:14.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:37:14.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:37:14.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:37:14.119 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:37:14.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:37:14.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:37:14.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:37:14.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:37:14.120 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:37:14.952 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:37:15.746 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:37:16.570 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:37:17.365 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:37:18.192 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:37:18.993 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:37:19.838 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:37:20.619 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:37:21.434 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:37:21.435 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:37:21.435 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:37:21.435 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:37:21.447 | 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

2025-09-01 17:37:21.448 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:37:21.567 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:37:21.661 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch595
2025-09-01 17:37:24.524 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 595/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 7.2, iou_loss: 2.7, l1_loss: 1.0, conf_loss: 2.8, cls_loss: 0.8, lr: 4.683e-07, size: 448, ETA: 0:01:55
2025-09-01 17:37:27.624 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 595/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 4.5, iou_loss: 1.7, l1_loss: 1.0, conf_loss: 1.3, cls_loss: 0.5, lr: 4.438e-07, size: 544, ETA: 0:01:52
2025-09-01 17:37:30.629 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 595/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.6, iou_loss: 2.2, l1_loss: 0.8, conf_loss: 1.9, cls_loss: 0.7, lr: 4.199e-07, size: 576, ETA: 0:01:49
2025-09-01 17:37:33.657 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 595/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.150s, data_time: 0.001s, total_loss: 5.4, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 1.3, cls_loss: 0.6, lr: 3.967e-07, size: 448, ETA: 0:01:46
2025-09-01 17:37:36.757 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 595/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 7.0, iou_loss: 2.4, l1_loss: 1.0, conf_loss: 3.0, cls_loss: 0.7, lr: 3.742e-07, size: 544, ETA: 0:01:43
2025-09-01 17:37:39.761 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 595/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.149s, data_time: 0.001s, total_loss: 5.0, iou_loss: 2.0, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.6, lr: 3.523e-07, size: 416, ETA: 0:01:39
2025-09-01 17:37:41.156 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:37:47.346 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:37:48.157 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:37:48.740 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6184
2025-09-01 17:37:48.882 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5357
2025-09-01 17:37:48.917 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4089
2025-09-01 17:37:48.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5210
2025-09-01 17:37:48.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:37:48.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:37:48.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.618
2025-09-01 17:37:48.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.536
2025-09-01 17:37:48.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.409
2025-09-01 17:37:48.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.521
2025-09-01 17:37:48.918 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:37:48.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:37:48.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:37:48.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:37:48.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:37:48.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:37:48.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:37:48.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:37:48.919 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:37:49.643 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:37:50.391 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:37:51.102 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:37:51.805 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:37:52.538 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:37:53.459 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:37:54.222 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:37:54.908 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:37:55.608 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:37:55.608 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:37:55.617 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:37:55.617 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:37:55.630 | 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

2025-09-01 17:37:55.631 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:37:55.752 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:37:55.885 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch596
2025-09-01 17:37:58.861 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 596/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.147s, 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: 3.218e-07, size: 480, ETA: 0:01:35
2025-09-01 17:38:02.016 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 596/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.156s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.1, l1_loss: 1.0, conf_loss: 1.5, cls_loss: 0.6, lr: 3.015e-07, size: 448, ETA: 0:01:32
2025-09-01 17:38:05.068 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 596/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.151s, data_time: 0.002s, total_loss: 5.6, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 1.6, cls_loss: 0.7, lr: 2.819e-07, size: 288, ETA: 0:01:29
2025-09-01 17:38:08.066 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 596/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.147s, data_time: 0.002s, total_loss: 5.4, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 2.629e-07, size: 448, ETA: 0:01:26
2025-09-01 17:38:11.004 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 596/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 7.4, iou_loss: 3.2, l1_loss: 1.2, conf_loss: 2.3, cls_loss: 0.7, lr: 2.447e-07, size: 416, ETA: 0:01:23
2025-09-01 17:38:14.123 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 596/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.153s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.7, l1_loss: 0.5, conf_loss: 1.0, cls_loss: 0.6, lr: 2.270e-07, size: 576, ETA: 0:01:20
2025-09-01 17:38:15.599 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:38:21.664 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:38:22.333 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:38:22.811 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6280
2025-09-01 17:38:22.900 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5406
2025-09-01 17:38:22.958 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4157
2025-09-01 17:38:22.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5281
2025-09-01 17:38:22.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:38:22.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:38:22.959 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.628
2025-09-01 17:38:22.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.541
2025-09-01 17:38:22.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-09-01 17:38:22.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.528
2025-09-01 17:38:22.960 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:38:22.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:38:22.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:38:22.961 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:38:22.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:38:22.962 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:38:22.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:38:22.963 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:38:22.964 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:38:23.635 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:38:24.186 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:38:24.763 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:38:25.316 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:38:25.871 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:38:26.483 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:38:27.053 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:38:27.609 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:38:28.171 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:38:28.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:38:28.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.53
2025-09-01 17:38:28.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:38:28.179 | 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: 7.03 ms

2025-09-01 17:38:28.180 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:38:28.293 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:38:28.367 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch597
2025-09-01 17:38:31.298 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 597/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 5.5, iou_loss: 2.3, l1_loss: 0.9, conf_loss: 1.6, cls_loss: 0.7, lr: 2.026e-07, size: 448, ETA: 0:01:15
2025-09-01 17:38:34.299 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 597/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.148s, data_time: 0.005s, total_loss: 5.6, iou_loss: 2.5, l1_loss: 0.7, conf_loss: 1.7, cls_loss: 0.6, lr: 1.866e-07, size: 320, ETA: 0:01:12
2025-09-01 17:38:37.277 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 597/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.148s, data_time: 0.001s, total_loss: 5.8, iou_loss: 2.6, l1_loss: 1.0, conf_loss: 1.6, cls_loss: 0.6, lr: 1.713e-07, size: 480, ETA: 0:01:09
2025-09-01 17:38:40.419 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 597/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.155s, data_time: 0.002s, total_loss: 3.4, iou_loss: 1.4, l1_loss: 0.5, conf_loss: 1.1, cls_loss: 0.4, lr: 1.566e-07, size: 480, ETA: 0:01:06
2025-09-01 17:38:43.541 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 597/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 10.0, iou_loss: 3.2, l1_loss: 1.1, conf_loss: 4.8, cls_loss: 0.8, lr: 1.425e-07, size: 288, ETA: 0:01:03
2025-09-01 17:38:46.614 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 597/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.151s, data_time: 0.001s, total_loss: 6.4, iou_loss: 2.4, l1_loss: 0.8, conf_loss: 2.6, cls_loss: 0.6, lr: 1.292e-07, size: 384, ETA: 0:01:00
2025-09-01 17:38:47.924 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:38:54.176 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:38:55.018 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:38:55.604 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6215
2025-09-01 17:38:55.718 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5317
2025-09-01 17:38:55.792 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4161
2025-09-01 17:38:55.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5231
2025-09-01 17:38:55.793 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:38:55.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:38:55.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.621
2025-09-01 17:38:55.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.532
2025-09-01 17:38:55.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.416
2025-09-01 17:38:55.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.523
2025-09-01 17:38:55.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:38:55.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:38:55.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:38:55.794 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:38:55.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:38:55.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:38:55.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:38:55.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:38:55.795 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:38:56.568 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:38:57.282 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:38:58.021 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:38:58.754 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:38:59.500 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:39:00.205 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:39:00.912 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:39:01.676 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:39:02.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:39:02.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.27
2025-09-01 17:39:02.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.52
2025-09-01 17:39:02.372 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:39:02.380 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.30 ms, Average NMS time: 0.91 ms, Average inference time: 7.21 ms

2025-09-01 17:39:02.381 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:39:02.461 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:39:02.544 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch598
2025-09-01 17:39:05.488 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 598/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 5.2, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 1.4, cls_loss: 0.7, lr: 1.109e-07, size: 384, ETA: 0:00:56
2025-09-01 17:39:08.504 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 598/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.148s, data_time: 0.003s, total_loss: 6.5, iou_loss: 2.6, l1_loss: 0.8, conf_loss: 2.3, cls_loss: 0.7, lr: 9.918e-08, size: 288, ETA: 0:00:53
2025-09-01 17:39:11.462 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 598/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.145s, data_time: 0.002s, total_loss: 4.8, iou_loss: 2.0, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.5, lr: 8.808e-08, size: 416, ETA: 0:00:49
2025-09-01 17:39:14.423 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 598/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 4.3, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.7, lr: 7.764e-08, size: 416, ETA: 0:00:46
2025-09-01 17:39:17.406 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 598/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 3.2, iou_loss: 1.5, l1_loss: 0.5, conf_loss: 0.8, cls_loss: 0.5, lr: 6.785e-08, size: 416, ETA: 0:00:43
2025-09-01 17:39:20.406 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 598/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.147s, data_time: 0.001s, total_loss: 4.9, iou_loss: 2.1, l1_loss: 0.8, conf_loss: 1.5, cls_loss: 0.6, lr: 5.872e-08, size: 288, ETA: 0:00:40
2025-09-01 17:39:21.730 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:39:27.989 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:39:28.935 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:39:29.594 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6300
2025-09-01 17:39:29.707 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5440
2025-09-01 17:39:29.783 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4267
2025-09-01 17:39:29.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5336
2025-09-01 17:39:29.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:39:29.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:39:29.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.630
2025-09-01 17:39:29.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.544
2025-09-01 17:39:29.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.427
2025-09-01 17:39:29.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.534
2025-09-01 17:39:29.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:39:29.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:39:29.784 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:39:29.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:39:29.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:39:29.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:39:29.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:39:29.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:39:29.785 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:39:30.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:39:31.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:39:32.213 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:39:33.039 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:39:33.826 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:39:34.642 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:39:35.435 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:39:36.265 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:39:37.057 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:39:37.058 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.28
2025-09-01 17:39:37.058 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.53
2025-09-01 17:39:37.058 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:39:37.065 | 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: 7.08 ms

2025-09-01 17:39:37.066 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:39:37.146 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:39:37.229 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch599
2025-09-01 17:39:40.207 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 599/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.146s, data_time: 0.002s, total_loss: 6.0, iou_loss: 2.6, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 0.7, lr: 4.666e-08, size: 544, ETA: 0:00:36
2025-09-01 17:39:43.472 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 599/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.161s, data_time: 0.002s, total_loss: 7.2, iou_loss: 3.2, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.8, lr: 3.915e-08, size: 256, ETA: 0:00:33
2025-09-01 17:39:46.462 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 599/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.147s, 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: 3.229e-08, size: 512, ETA: 0:00:30
2025-09-01 17:39:49.585 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 599/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.154s, 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: 2.610e-08, size: 352, ETA: 0:00:27
2025-09-01 17:39:52.550 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 599/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.6Gb, iter_time: 0.145s, data_time: 0.001s, total_loss: 6.1, iou_loss: 2.2, l1_loss: 0.9, conf_loss: 2.3, cls_loss: 0.7, lr: 2.056e-08, size: 448, ETA: 0:00:24
2025-09-01 17:39:55.501 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 599/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.146s, data_time: 0.001s, total_loss: 6.0, iou_loss: 2.2, l1_loss: 0.7, conf_loss: 2.1, cls_loss: 1.1, lr: 1.569e-08, size: 288, ETA: 0:00:21
2025-09-01 17:39:56.844 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:40:03.048 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:40:03.868 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:40:04.447 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6221
2025-09-01 17:40:04.561 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5434
2025-09-01 17:40:04.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4115
2025-09-01 17:40:04.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5257
2025-09-01 17:40:04.596 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:40:04.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:40:04.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.622
2025-09-01 17:40:04.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-09-01 17:40:04.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.412
2025-09-01 17:40:04.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.526
2025-09-01 17:40:04.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:40:04.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:40:04.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:40:04.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:40:04.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:40:04.597 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:40:04.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:40:04.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:40:04.598 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:40:05.343 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:40:06.050 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:40:06.790 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:40:07.494 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:40:08.188 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:40:08.923 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:40:09.627 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:40:10.358 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:40:11.055 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:40:11.055 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.28
2025-09-01 17:40:11.055 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.53
2025-09-01 17:40:11.055 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:40:11.062 | INFO     | yolox_microbt.core.trainer:evaluate_and_save_model:368 - 
Average forward time: 6.39 ms, Average NMS time: 0.91 ms, Average inference time: 7.30 ms

2025-09-01 17:40:11.063 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:40:11.150 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:40:11.232 | INFO     | yolox_microbt.core.trainer:before_epoch:190 - ---> start train epoch600
2025-09-01 17:40:14.127 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 600/600, iter: 20/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.142s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.5, conf_loss: 1.2, cls_loss: 0.6, lr: 9.787e-09, size: 448, ETA: 0:00:16
2025-09-01 17:40:17.134 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 600/600, iter: 40/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 3.9, iou_loss: 1.6, l1_loss: 0.8, conf_loss: 1.0, cls_loss: 0.5, lr: 6.525e-09, size: 512, ETA: 0:00:13
2025-09-01 17:40:20.211 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 600/600, iter: 60/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.152s, data_time: 0.002s, total_loss: 7.6, iou_loss: 2.6, l1_loss: 1.1, conf_loss: 3.2, cls_loss: 0.7, lr: 3.922e-09, size: 512, ETA: 0:00:10
2025-09-01 17:40:23.337 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 600/600, iter: 80/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.154s, data_time: 0.002s, total_loss: 4.2, iou_loss: 1.9, l1_loss: 0.6, conf_loss: 1.2, cls_loss: 0.5, lr: 1.978e-09, size: 448, ETA: 0:00:07
2025-09-01 17:40:26.365 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 600/600, iter: 100/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.149s, 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: 6.928e-10, size: 512, ETA: 0:00:04
2025-09-01 17:40:29.394 | INFO     | yolox_microbt.core.trainer:after_iter:258 - epoch: 600/600, iter: 120/129, gpu mem: 1330Mb, mem: 46.7Gb, iter_time: 0.149s, data_time: 0.002s, total_loss: 5.7, iou_loss: 2.1, l1_loss: 0.7, conf_loss: 2.3, cls_loss: 0.6, lr: 6.672e-11, size: 448, ETA: 0:00:01
2025-09-01 17:40:30.809 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:40:37.089 | INFO     | yolox.evaluators.voc_evaluator:evaluate_prediction:144 - Evaluate in main process...
2025-09-01 17:40:37.909 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.50
2025-09-01 17:40:38.426 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for person = 0.6260
2025-09-01 17:40:38.579 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for car = 0.5435
2025-09-01 17:40:38.614 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:324 - AP for dog = 0.4130
2025-09-01 17:40:38.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:329 - Mean AP = 0.5275
2025-09-01 17:40:38.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:330 - ~~~~~~~~
2025-09-01 17:40:38.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:331 - Results:
2025-09-01 17:40:38.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.626
2025-09-01 17:40:38.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.543
2025-09-01 17:40:38.615 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:333 - 0.413
2025-09-01 17:40:38.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:334 - 0.527
2025-09-01 17:40:38.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:335 - ~~~~~~~~
2025-09-01 17:40:38.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:336 - 
2025-09-01 17:40:38.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:337 - --------------------------------------------------------------
2025-09-01 17:40:38.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:338 - Results computed with the **unofficial** Python eval code.
2025-09-01 17:40:38.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:339 - Results should be very close to the official MATLAB eval code.
2025-09-01 17:40:38.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:340 - Recompute with `./tools/reval.py --matlab ...` for your paper.
2025-09-01 17:40:38.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:341 - -- Thanks, The Management
2025-09-01 17:40:38.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:342 - --------------------------------------------------------------
2025-09-01 17:40:38.616 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.55
2025-09-01 17:40:39.377 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.60
2025-09-01 17:40:40.088 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.65
2025-09-01 17:40:40.797 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.70
2025-09-01 17:40:41.544 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.75
2025-09-01 17:40:42.244 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.80
2025-09-01 17:40:42.953 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.85
2025-09-01 17:40:43.698 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.90
2025-09-01 17:40:44.414 | INFO     | yolox_microbt.data.datasets.voc:_do_python_eval:304 - Eval IoU : 0.95
2025-09-01 17:40:45.172 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:252 - --------------------------------------------------------------
2025-09-01 17:40:45.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:253 - map_5095: 0.28
2025-09-01 17:40:45.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:254 - map_50: 0.53
2025-09-01 17:40:45.173 | INFO     | yolox_microbt.data.datasets.voc:evaluate_detections:255 - --------------------------------------------------------------
2025-09-01 17:40:45.180 | 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

2025-09-01 17:40:45.190 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:40:45.275 | INFO     | yolox_microbt.core.trainer:save_ckpt:389 - Save weights to ./YOLOX_outputs/sa6921_vnne_3classes_voc_quant_a8w4_600e_185k_trainset_fusebn_ranger
2025-09-01 17:40:45.356 | INFO     | yolox_microbt.core.trainer:after_train:172 - Training of experiment is done and the best AP is 30.82
